physics.med-ph

0

eess.IV 2026-07-03

Self-auditing drift model leads SSIM in accelerated knee MRI

by Qing Lyu, Jianxu Wang +3 more

Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI

It adds per-slice risk scores that flag unreliable outputs while preserving lesion detail at high acceleration.

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Accelerated magnetic resonance imaging reduces acquisition time, but reconstruction from undersampled k-space can blur diagnostically relevant structures or introduce failures that are not captured by global image metrics. We propose SA-RDM-DC, a Self-Auditing Residual generative Drifting Model with Data Consistency for accelerated knee MRI. The method adapts the newly proposed generative drifting paradigm to accelerated MRI by training a physics-conditioned drift field from the zero-filled reconstruction toward the fully sampled residual correction. It predicts image- and missing-k-space residual corrections, enforces data consistency with acquired k-space, uses frequency-aware and residual drifting supervision to recover fine detail, and produces dense error maps and slice-level risk scores in the same inference pass. We evaluate SA-RDM-DC on multi-coil fastMRI knee data at acceleration factors of 4, 8, and 12, with fastMRI+ pathology annotations for region-level and classifier-based task preservation, and on SKM-TEA for zero-shot and fine-tuned protocol-shift evaluation. Compared with zero-filled reconstruction, UNet-image-SENSE, DC-UNet, Score-Diffusion, ELF-Diff, SENSE-VarNet, and MoDL baselines, SA-RDM-DC achieves the highest SSIM across fastMRI acceleration factors while retaining subsecond per-slice inference and avoiding the long sampling time of iterative diffusion baselines. In pathology-aware analysis, SA-RDM-DC preserves lesion-region structural fidelity and reduces meniscus prediction instability. Its self-auditing scores strongly identify high-error reconstructions on fastMRI and partially transfer as a selective-review signal under SKM-TEA protocol shift. These results support reconstruction evaluation that jointly considers image fidelity, pathology preservation, runtime, and case-specific reliability.

0

physics.med-ph 2026-07-03

Free-breathing lung T1 and T2 maps achieved at 0.55T

by Pavlos Panos, Oliver Bieri +3 more

Free-breathing Pulmonary Relaxometry at 0.55T

Automated motion correction and segmentation enable quantitative relaxometry without breath-holds.

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Purpose: To evaluate the feasibility of an integrated, free-breathing workflow for automated 2D pulmonary relaxometry (T1, T2) at 0.55T. Methods: A 2D inversion recovery ultra-fast balanced steady-state free precession (IR-uf-bSSFP) sequence was adapted to achieve high-temporal sampling of the transient phase at 0.55T. The technique was validated in a phantom and tested in eight healthy volunteers as well as one patient. A fully automated pipeline was developed, featuring multi-contrast registration for motion correction and deep learning based lung segmentation to enable voxel-wise nonlinear fitting for T1 and T2 map generation. Results: Phantom results were in close agreement with reference scans. In-vivo, the proposed free-breathing framework effectively mitigated respiratory motion, yielding quantitative maps in close agreement with breath-hold references. Healthy lung parenchyma relaxation times were T1 = (930+-40)ms and T2 = (90+-8)ms. In a patient case, the method successfully distinguished a solid lung mass from healthy parenchyma, with the lesion showing elevated T1 (960ms vs 810ms in the surrounding parenchyma). Conclusions: Simultaneous free-breathing T1 and T2 mapping of the lung is feasible at 0.55T using a fully automated pipeline. By eliminating breath-holds and external gating, this approach improves patient compliance and potentially facilitates the use of quantitative lung MRI in routine clinical practice.

0

physics.med-ph 2026-07-03

ML segmentation reduces colon MRI analysis time by over 80%

by Abi Spicer, Stephen Lloyd-Brown +7 more

Accelerating MRI Colon Volume Measurements and Reducing Inter-Observer Variation through Automatic Segmentation and Human-in-the-Loop Correction

Corrected automatic masks match manual accuracy for colon volumes with much less work and higher consistency.

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The movement distribution, and volume of both chyme and gas in the colon, are important metrics to understand colonic function in health, disease, and the effects of treatments and different foodstuffs. Current methods available for assessment of these colonic contents using MRI consist mainly of manual segmentation or semi-automatic segmentation. However, these methods of segmentation are very labour intensive and too slow for clinical applications, require expert knowledge and some semi-automatic methods require use of bowel preparation. MRI scans were acquired in 2 breath holds using mDIXON sequences. We used the 'No New U-Net' (nnU-Net) ML model to automatically segment the colon, including colonic regions (ascending, transverse, descending and sigmoid-rectal). The ML-generated masks were corrected manually and the time taken for correction was recorded. ML segmentations were compared to both manual segmentations and observer corrected ML (CorrML) segmentations. Observer repeatability was also evaluated for both manual and CorrML methods to create a benchmark for the allowable error in the automatic segmentations. Analysis time was significantly reduced (p<0.0001) from 56 mins (+-11 mins (SD)) for manual masks to 11 mins (+-5 mins (SD)) for CorrML masks. Both DICE and ICC values showed excellent agreement between manual, ML and CorrML segmentations for whole colonic volume (ICC = 0.96) whilst regional volumes were good-excellent (ICC = 0.80-0.95). Inter-observer repeatability was improved when using CorrML methods over manual segmentation (ICC manual > 0.89, CorrML > 0.93). Analysis time was reduced by over 80% when using CorrML methods and whole colonic volumes measured by ML would be suitable for use with minimal checks. Hence the methods proposed here would be clinically useful.

0

physics.med-ph 2026-07-03

Cohort-aware network reaches 0.72 AUC for toxicity prediction

by Haotian Feng, Yan Kong +2 more

Prediction of Radiotherapy-Induced Hematologic Toxicity in Cervical Cancer with Cohort-Aware Framework

It outperforms data pooling and harmonization by learning cohort-specific representations to handle segmentation differences in cervical can

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Hematologic toxicity (HT) is a major dose-limiting complication of pelvic radiotherapy for cervical cancer. Although radiomic and dosiomic features improve HT prediction beyond dosimetric metrics, their performance is highly sensitive to contour variability, limiting generalizability. We developed a cohort-aware representation-learning framework to address this challenge. We retrospectively analyzed 152 cervical cancer patients treated with pelvic radiotherapy without concurrent chemotherapy. Patients were divided into two cohorts based on the operators performing pelvic bone segmentation. HT prediction models were developed using cohort-specific training, pooled training, statistical harmonization, and a cohort-aware neural network (CANN) that learns shared and cohort-specific representations with contrastive regularization. Performance was evaluated using cross-validation and an independent test set. Cohort-specific models achieved test AUCs of 0.77 and 0.71, outperforming a dosimetry-only model (AUC=0.58). Directly pooling cohorts reduced performance (test AUC=0.64). Statistical harmonization provided limited benefit, while adversarial and correlation-based alignment further degraded performance. CANN achieved the best balance between robustness and generalizability (test AUC=0.72), with ablation studies confirming the importance of cohort-specific representations and contrastive alignment. These results demonstrate that cohort-aware representation learning effectively mitigates contour variability and improves the generalizability of radiomic and dosiomic models for HT prediction.

0

physics.med-ph 2026-07-02

Coupled models cut MRI treatment prediction errors

by James Grover, Emily A. Hewson +6 more

Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI

A closed loop between patient-specific forecasts and foundation-model checks lowers dosimetric mistakes at 400 ms horizons.

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Image-guided therapies, including radiotherapy, biopsy and deep brain stimulation, rely on real-time targeting of anatomical structures. However, in the presence of motion, imaging latencies create a temporal misalignment between observed and true anatomy, compromising treatment accuracy. Artificial intelligence-based frameworks have increasingly been presented to close this latency gap, but leading personalised models can fail due to a lack of stable anatomical grounding. Foundation models can provide grounded behaviour, but they do not adapt to real-time, individual patient dynamics. Here we introduce a closed-loop coupling framework that synergises patient-specific temporal prediction with continuous segmentation-based anatomical interpretation from a foundation model. A personalised model predicts future anatomy to compensate for system latency, while a streaming foundation model provides anatomical supervision used to continuously update the temporal predictor in real time during treatment. We validate the framework using a digital phantom and intrafraction magnetic resonance imaging (MRI) from patients undergoing MRI-guided radiotherapy. For a prediction horizon of 400 ms, the proposed method improves anatomical prediction and reduces dosimetric error compared with existing approaches, within clinically relevant latency constraints. These results establish closed-loop coupling as a general strategy for real-time image-guided intervention.

0

physics.med-ph 2026-06-30

Neural nets reconstruct proton tracks in pCT detectors

by B. Dudás, G. Papp +2 more

Compact deep learning pipeline for particle track reconstruction in the pCT detector system

Compact models trained on simulations deliver accurate trajectories and energies within clinical time limits.

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Proton computed tomography (pCT) requires both fast and accurate reconstruction of particle trajectories and kinetic energies to achieve clinically viable image formation. Traditional distance-based matching algorithms often fail under the combined effects of multiple Coulomb scattering and track crossings and most importantly many of them take too much computation time, motivating the use of lightweight deep learning models that can be evaluated rapidly. In this work, we develop a two-stage reconstruction pipeline consisting of (i) a neural-network-assisted tracking module and (ii) a kinetic-energy estimation model. For the tracking task, compact multilayer perceptrons are trained to predict the expected hit position in the subsequent detector layer, providing a physically informed prior that substantially reduces ambiguities in bipartite matching. Furthermore, ambiguous tracks are flagged and excluded from the final analysis. Our training data is provided by OpenGATE simulation toolkit, both for tracking and energy estimation, where we designed a fully connected network that processes detector hit information. This model predicts the incoming proton kinetic energy with sufficient accuracy for current pCT image reconstruction methods. The entire pipeline benefits from deep-learning parallelism and evaluates particle tracks fast enough for clinical time constraints. Together, these results demonstrate that compact deep learning models can reliably reconstruct particle trajectories and energies in a realistic pCT detector system, offering a computationally efficient and highly accurate alternative to traditional matching and tracking methods.

0

physics.med-ph 2026-06-30

Physics-guided harmonics separate motion from wrist PPG

by Nouhaila Fraihi, Ouassim Karrakchou +1 more

Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography

Accelerometer-conditioned decomposition isolates heart and breathing signals, outperforming prior methods on motion-heavy recordings.

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Wrist-worn photoplethysmography (PPG) enables continuous monitoring of cardiopulmonary physiology, but reliable heart rate (HR) and respiratory rate (RR) estimation in free-living conditions remains challenging due to non-stationary motion artifacts that spectrally overlap with physiological dynamics. Existing signal-processing methods degrade under strong motion, while unconstrained deep learning approaches often lack physiological interpretability and identifiable structure. We propose a Physically-Constrained Harmonic Separation (PCHS) framework that formulates HR and RR estimation from wrist PPG as an analysis-by-synthesis problem, where accelerometer measurements condition artifact separation rather than directly regressing vital signs. A physics-guided harmonic generator decomposes the observed signal into quasi-periodic physiological components and a motion-related residual, enabling HR recovery from the fundamental frequency and RR prediction from respiratory-driven modulations of the harmonic parameters. Robust reconstruction objectives, separation constraints, and uncertainty-aware weighting stabilize the decomposition under motion. Experiments on the motion-intensive PPG-DaLiA dataset demonstrate that PCHS outperforms state-of-the-art methods while yielding interpretable signal decompositions that effectively disentangle physiological activity from motion artifacts.

0

physics.med-ph 2026-06-30

Adversarial training cuts CT model attack success to 7%

by Filippo Maria Balli, Giorgia Stendardo +7 more

Adversarial robustness of a U-Net-based model observer for CT protocol optimization

Dynamic training on U-Net observer protects low-contrast object detection and localization without performance loss

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Artificial intelligence is increasingly used in medical imaging, yet its robustness to input perturbations remains a critical concern for a wide clinical adoption. To this end, we used adversarial examples to systematically probe vulnerabilities of a U-Net-based model observer for computed tomography protocol optimization, performing detection and localization of low-contrast objects in a phantom dataset. Adversarial attacks were generated using both gradient-based and optimization-based white-box methods. Fast gradient perturbations produced high misclassification rates, reaching up to 75% at intermediate perturbation levels while remaining visually imperceptible. Localization was more robust, with success rates of about 25% for small perturbations and 42% at moderate levels. In contrast, optimization-based attack achieved success rates close to 50% for both tasks. To mitigate these vulnerabilities, dynamic adversarial training was implemented. This reduced the success rate of optimization-based attacks to 7% for classification and 13% when including localization-specific training, demonstrating a substantial robustness improvement without compromising task performances, confirmed by localization receiver operating characteristic analysis. To further interpret model behavior, radiomic texture analysis was performed on original and adversarial images. While most global image statistics remain stable, specific texture-related features exhibit consistent changes in successful attacks, highlighting the model's sensitivity to subtle local intensity patterns. Overall, adversarial training improves robustness without degrading performance, while radiomic analysis reveals interpretable links between texture alterations and prediction failures, supporting more reliable and explainable AI systems for medical imaging.

0

physics.med-ph 2026-06-30

Three classes sort radiation effect models by cluster rationale

by Hans Rabus

Prediction of biological radiation effects based on ionization clusters (nanodosimetry)

Review groups approaches linking nanodosimetry to biological outcomes according to single-target, pair-synergy or fluence logic

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This article reviews approaches that link the formation of ionization clusters in nanometric volumes to radiobiological effectiveness. The corresponding models are presented using harmonized terminology and notation. They are categorized into three classes according to the most important, often implicit model rationale: (a) models that use a nanodosimetric weighting factor for biological effectiveness derived from frequency distributions of ionization clusters in a single target; (b) models that account for the synergistic effects of pairs of ionization clusters formed in different targets; (c) models that account for 'macroscopic' situations involving many nanometric targets and derive radiation quantities from the particle fluence. Further conceptual differences between the models and their underlying assumptions are discussed, such as the fact that some models are mechanistic while others only aim to elucidate correlations. Eventually, an attempt is made to identify the key open questions in this field that still need to be addressed.

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physics.med-ph 2026-06-29

One sheath maps heart signals and delivers ablation catheters

by Qindong Zheng, Anil Demircali +15 more

A Mapping Sheath with Thermally Drawn Multi-Electrode Basket for Cardiac Electrophysiological Recording and Ablation Catheter Delivery

Thermally drawn basket electrodes allow navigation, recording, and therapy in single device, shown feasible in pig hearts.

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Cardiac arrhythmias, particularly atrial fibrillation, represent a major cardiovascular health burden and underscore the need for efficient and integrated strategies for electrical mapping and targeted therapy. Cardiac electrophysiology procedures depend on accurate identification of arrhythmogenic substrates followed by timely catheter ablation, but conventional diagnostic and therapeutic devices remain separate, often requiring repeated catheter exchanges and multiple access routes. Here, we report an adaptable strategy for functionalizing hollow-core sheaths with EP mapping capabilities, integrating multielectrode recording and ablation catheter delivery within a single compact platform. The device leverages thermal drawing to enable complex geometric fabrication, miniaturization, rapid prototyping, and scalable manufacturing of ultrathin electrode splines arranged circumferentially at the distal end to form an adjustable basket. The mapping sheath exhibited mechanical and electrophysiological properties suitable for intracardiac navigation and electrogram recording in bench-top evaluations, an in vitro left atrial phantom study, and ex vivo Langendorff-perfused porcine heart testing. In vivo porcine studies further demonstrated translational feasibility through vascular introduction, fluoroscopic visualization, intracardiac deployment, tissue contact, electrogram acquisition, and reconstruction of voltage and activation maps. These results support the development of intracardiac platforms with an adapted manufacturing approach, potentially guiding advances in agile cardiac mapping and ablation.

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physics.med-ph 2026-06-29

Annihilation photons show intermediate entanglement in human liver and spleen

by Pawel Moskal, Deepak Kumar +38 more

First-in-human quantum entanglement imaging

First in-vivo scan extracts a polarization correlation between separable and maximally entangled limits using plastic-scintillator detectors

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Annihilation photons are quantum-entangled in polarization, a phenomenon that has not been exploited in medical diagnostics so far. We present the first in vivo imaging of the degree of quantum entanglement of photons originating from positron-electron annihilation within a human subject. This study utilized the Jagiellonian Positron Emission Tomography (J-PET) scanner, constructed from plastic scintillators. In plastics, annihilation photons interact primarily via the Compton effect, which provides simultaneous information regarding the photon interaction position and time, as well as the photon polarization plane. The patient was injected with a DOTA-TATE radiopharmaceutical labeled with the $^{68}$Ga radionuclide. Using the J-PET scanner, we determined the image of the radiopharmaceutical uptake and, simultaneously, the image of the degree of quantum entanglement. The latter was determined from the relative angle between the polarization planes of the annihilation photons. The values of the degree of quantum entanglement extracted for the liver and the spleen are smaller than those predicted for maximally entangled two-photon states, yet larger than expected for separable photons. This demonstration opens new perspectives for the application of quantum entanglement in clinical diagnostics.

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physics.med-ph 2026-06-29

Single-DOF model sustains phonation oscillation with added forces

by Sardar Nafis Bin Ali, Maryam Naghibolhosseini +1 more

An Optimal Contact-Mechanically Consistent and Flow-Separation Adapted Modeling of Vocal Fold Dynamics

Resistance and closure terms let a damped mass-spring system match subject glottal waveforms to under 3 percent error without vocal-tract co

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Single mass-spring-damper models of vocal folds have been effective in simulating vocal fold vibrations without added complexity. However, single-degree-of-freedom models cannot sustain oscillation in the presence of structural damping unless source-tract interaction is considered. Moreover, existing lumped models struggle to accurately simulate vocal fold closure during phonation. This study aims to develop a reliable and simplified single-degree-of-freedom model of phonation that can simulate sustained oscillation in a damped system without incorporating a vocal tract model. Additionally, the proposed model maintains vocal fold closure in a manner consistent with the physics of phonation, addressing a longstanding challenge in existing lumped models. High-speed videoendoscopy (HSV) data from four normophonic subjects producing sustained vowel /i/ were used to extract glottal area waveforms (GAWs) via deep learning-based image segmentation for particle swarm optimization of the model parameters. An additional resistance force was incorporated to compensate for flow separation and generate the force imbalance required for sustained oscillation. An external structural force was also added during closure to sustain the closed phase. The 4th-order Runge-Kutta method was used to solve the governing equations with enhanced numerical stability and accuracy. The model parameters were optimized for individual subjects, resulting in normalized errors below 3% between experimental and simulated GAWs. The proposed model accurately reproduced subject-specific vocal fold vibrations and vocal fold closure in agreement with experimental data. Overall, the proposed model provides a computationally efficient framework for simulating sustained phonation without requiring complex source-tract coupling while capturing the key biomechanical and aerodynamic mechanisms of phonation.

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physics.med-ph 2026-06-29

NGSE-Corr identifies most precise imaging method in 95% of trials

by Yan Liu, Ziping Liu +5 more

NGSE-Corr: A technique for objective clinical evaluation of quantitative-imaging methods without a gold standard

The technique uses measured patient values and a linear noise model to rank quantitative imaging methods, matching true rankings 91% of the

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Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates QI methods without true values. The technique assumes a linear stochastic relationship between true and measured values, characterized by a slope, bias, and multivariate Gaussian-distributed noise term that models correlated noise across QI methods. We derive a maximum-likelihood approach to estimate these parameters using only measured values. From the estimates, we compute noise-to-slope ratio (NSR) to rank QI methods based on precision. Numerical experiments showed that NGSE-Corr reliably estimated the NSR, accurately ranked methods, and maintained performance even when assumptions made by the technique were partially violated. We also validated NGSE-Corr in an in silico imaging trial to rank three quantitative SPECT methods for measuring regional activity uptake in patients with bone metastatic castrate-resistant prostate cancer treated with radium-223. NGSE-Corr correctly identified the most precise QI method and ranked the methods for 95% (95% CI, 89%-98%) and 91% (95% CI, 84%-95%) of trials, respectively, with data from 50 patients. Performance further improved with larger cohorts. With 200 patients, NGSE-Corr yielded same rankings as those obtained with true values across all trial instances. These findings demonstrate the ability of NGSE-Corr to accurately rank QI methods without gold standards and motivate clinical validation and broader applications.

0

q-bio.QM 2026-06-29

MCIDs set for smartphone gait metrics in progressive MS

by Mike D Rinderknecht, Bernhard Fehlmann +8 more

Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis

Anchor-based analysis of 243 patients yields thresholds including -0.16 m/s step velocity to interpret meaningful change.

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Background: Digital health technologies allow for frequent, remote gait monitoring in people with multiple sclerosis (MS). However, to differentiate daily variability from actual disease progression in longitudinal data, established minimal clinically important differences (MCID) are required. Currently, there is limited literature defining these thresholds for digital gait metrics. Objective: To establish MCIDs for digital gait measures reflecting progression in MS. Methods: Digital gait measures were captured via daily, remote, smartphone-based Two-Minute Walk Tests in CONSONANCE (NCT03523858), a phase 3b study of ocrelizumab in progressive MS. Using an anchor-based approach, median changes from baseline at Week 96 on digital gait measures were computed for patients showing clinically meaningful worsening on either Timed 25-Foot Walk, Ambulation Score, Expanded Disability Status Scale, or 12-item Multiple Sclerosis Walking Scale. These changes were subsequently triangulated to derive the MCID estimates. Results: 243 patients with progressive MS (female: n=125 (51%); mean [SD] age: 49.3 [9.3]; mean [SD] EDSS: 4.8 [1.4]) had digital gait data available at baseline and Week 96. Median changes were generally consistent across anchors. Triangulated MCIDs are: Step Velocity = -0.16 m/s, Step Velocity Scaled to Walking Time = -0.18 m/s, Step Duration = 0.06 s, Step Length = -0.07 m, Total Number of Steps = -28, and Total Distance Walked = -24 m. Conclusion: These MCIDs provide a framework for interpreting meaningful gait changes and integrating digital measures into MS outcome evaluation. Beyond facilitating novel clinical trial endpoints to evaluate treatment efficacy, they enable objective, real-world monitoring to advance personalized patient care.

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physics.comp-ph 2026-06-26

Superiorization cuts noise and dose versus feasibility-seeking alone

by Tobias Becher, Yair Censor +2 more

GPU-accelerated superiorization on constrained physical problems with SupPy

Open GPU-enabled Python toolbox improves three physics applications and works on infeasible constraint sets

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The superiorization method (SM) is situated between feasibility-seeking and constrained optimization. Instead of aiming at the minimum of a given objective function over a constraint set, it seeks a feasible point at which the objective function value is reduced - though not necessarily minimal - rather than hard targets, or in which a mathematically optimal solution is not strictly required. While the method has been investigated for several applications in physics, its broader use has been limited, in part due to the lack of openly available software for researchers wishing to explore it. In this work we apply superiorization to three problems from applied physics: seismic image reconstruction, low-dose CT reconstruction and intensity-modulated radiotherapy treatment planning. These experiments are conducted with SupPy, an open-source modularized Python toolbox developed for this work, which supports execution of feasibility-seeking algorithms and their superiorized version on both the CPU and the GPU. In all three cases the superiorized algorithms achieve favorable results compared to feasibility-seeking alone, with reduced noise in the imaging examples and lowered body dose in the radiotherapy plans. For the radiotherapy case we further observe that superiorization produces clinically viable plans on infeasible constraint sets.

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physics.med-ph 2026-06-25

Optoacoustic signals predict laser tattoo removal endpoints

by Sergio Contador, Rodrigo Rojo +2 more

On the link between optoacoustic imaging and selective photothermolysis

The correlation with immediate clinical results suggests a route to objective parameter selection that shortens sessions.

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Selective photothermolysis (SP) is widely used in clinical and cosmetic dermatology to remove unwanted skin structures. Careful laser parameter selection results in safe and effective target removal. Nevertheless, parameter selection relies on a trial-and-error process based on visual inspection of the immediate skin response. This process is highly dependent on the practitioner experience and can be time-consuming. SP and optoacoustic imaging (OI) share many physical principles. However, the possibility of using OI to improve laser parameter selection in SP has not been studied before. Here, we explore the relationship between OI and SP theoretically and through clinical in-human trials with a focus on tattoo removal. Our results demonstrate a strong correlation between OI signals acquired before and after treatment with the immediate clinical endpoint, suggesting that OI could be used as a tool for optimal parameter selection and reduced treatment duration in tattoo removal and other SP treatments.

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physics.ins-det 2026-06-25

Entangled photon pairs raise PET sensitivity up to 10%

by Mihael Makek, Ana Marija Kožuljević +5 more

Positron Emission Tomography with quantum-entangled Compton events: first imaging results at clinically relevant activities

First images at clinical activities show 20% better signal-to-background ratio for polarization-correlated Compton events while preserving u

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In Positron Emission Tomography, a potential, yet unutilized enhancement, may come from exploiting the quantum entanglement of the annihilation quanta, inscribed in the correlation of their polarizations. To investigate this, we built a PET demonstrator capable of measuring polarization correlations of annihilation quanta by their Compton scattering, based on single-layer scintillator polarimeters. We present a detailed study of the imaging of two $^{68}$Ge line sources, 45 MBq each, to extract the spatial resolution and assess image quality. The results show that a spatial resolution of 2.5$\pm$0.1 mm is obtained using single-pixel events, while resolutions obtained with polarization-correlated Compton events range from 3.6$\pm$0.3 mm to 4.9$\pm$0.3 mm, depending on data selection criteria. We also found that the polarization-correlated Compton events exhibit up to 20% higher average signal to random background ratio compared to the single-pixel events. We also present the first imaging of the NEMA NU-4 phantom filled with a $^{68}$Ga solution of 378 MBq initial activity, successfully combining polarization-correlated events with conventional single-pixel event selection. Based on the extracted spatial resolution, signal-to-background, signal-to-noise, contrast, and contrast-to-noise ratio, we estimate that up to 10% sensitivity increase may be attained by exploiting the polarization-correlated events, while preserving a high image quality.

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physics.flu-dyn 2026-06-25

Clockwise spin reverses propeller thrust at intermediate Re

by Rong Fu, Siyu Li +1 more

From Propulsion to Suction: Unraveling Thrust Reversal in Propellers at Intermediate Reynolds Numbers

Centrifugal suction drives backward motion from Re 1.3 to 150 when it overpowers the usual forward push.

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This study investigates propeller hydrodynamics at intermediate Reynolds numbers (Re), crucial for small-scale robotic systems but still uncharted. Experiments on a propeller-driven underwater vehicle and numerical simulations reveal thrust reversal--a phenomenon where clockwise propeller rotation leads to backward motion--in the approximate range 1.3 < Re < 150 under specific conditions. Notably, counterclockwise rotation consistently results in backward motion. Simulations reveal that this behavior arises when centrifugal suction, an inward force along the axis caused by radial outward flow from the propeller's rotation, dominates over fluid backward acceleration, the primary thrust mechanism at high Re. These findings provide critical insights into the unique dynamics of the intermediate Re regime and inform the design of efficient propulsion systems for miniature aquatic robots.

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cs.CV 2026-06-24

Multilevel stochastic PnP speeds SVCT reconstruction while matching quality

by Antoine De Paepe, Alexandre Bousse +1 more

Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction

Wavelet approximation spaces make the prior-coherence correction vanish in expectation, skipping costly fine-scale gradient estimates.

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Sparse-view computed tomography (SVCT) reduces radiation exposure and acquisition time, but the limited number of projection views makes the reconstruction problem severely ill-posed and leads to streak artifacts when analytical methods are used. Plug-and-Play (PnP) methods provide an effective way to combine data fidelity with learned image priors, while stochastic PnP methods further improve robustness by matching the denoiser input distribution through re-noising. However, these methods often require many iterations to converge, which limits their practical efficiency. In this work, we propose a multilevel (ML) stochastic PnP method for SVCT that accelerates stochastic PnP reconstruction. We highlight that, in the stochastic setting, directly enforcing prior coherence across levels would require accurately estimating fine-level prior gradients through multiple denoiser function evaluations, which substantially increases the computational cost. Motivated by this observation, we perform the multilevel steps in multiresolution analysis (MRA) approximation spaces. This choice is supported by the structure of the wavelet decomposition, which causes the prior-coherence correction to vanish in expectation, thereby avoiding costly estimation of fine-level stochastic prior gradients for the coarse-level corrections. Experiments on SVCT reconstruction show that our method, called Multilevel Stochastic Plug-and-Play (ML-SPnP), achieves reconstruction quality comparable to state-of-the-art methods while substantially reducing runtime.

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physics.ins-det 2026-06-24

Electron attachment governs CCE loss at ultra-high dose rates

by Pierre Gérard Ortega, Gilles De Lentdecker +2 more

Charge Collection Efficiency in Air-Vented Plane-Parallel Ionisation Chambers at Ultra-High Dose Rates: A Self-Consistent Garfield++ Monte Carlo Model Including Space-Charge Effects and Ion Recombination

Simulations show field distortions lower free electron fraction and thereby control recombination in ionisation chambers

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Ultra-high dose rate (UHDR) irradiation used in FLASH radiotherapy induces strong space-charge effects in plane-parallel ionisation chambers (PPICs), leading to significant reductions in charge collection efficiency (CCE). To investigate these effects, we extended the Garfield++ framework by implementing ion-ion recombination and self-consistent space-charge electric field calculations. The developed Monte Carlo model couples particle transport, electron attachment, recombination processes, and dynamic electric-field distortions. The implementation was validated against analytical and numerical models from the literature, including the works of Fenwick and Kumar, Kranzer et al., and Paz Mart\'in et al., with excellent agreement for the free electron fraction (FEF), CCE, induced current, and electric field evolution. The simulations show that space charge can locally increase the electric field by more than a factor of four or reduce it to nearly zero. The results suggest that CCE reduction under UHDR conditions is mainly driven by the decrease of the FEF caused by electric-field-dependent electron attachment, indicating that recombination may be largely governed by FEF evolution. This opens promising perspectives for improved analytical models and real-time correction methods for ionisation chamber dosimetry under UHDR conditions.

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physics.med-ph 2026-06-24

Unpaired model turns standard CT into photon-counting material maps

by Ruihan Huang, Staffan Holmin +1 more

Unpaired Deep Learning Synthesis of Photon-Counting CT Material Basis Maps from Non-contrast Energy-Integrating Abdominal CT Images

DDIM plus domain-adversarial U-Net predicts water and iodine maps from public EID scans without any paired training examples.

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Photon-counting Computed Tomography (PCCT) is the most advanced Computed Tomography (CT) technology, offering significant improvements in image quality and diagnostic capabilities. However, since PCCT has only recently been adopted in the clinc, there are no publicly available PCCT image datasets for study. We therefore aim to synthesize PCCT spectral material-basis images from publicly available EID CT images. We propose a two-step deep learning model designed to synthesize photon-counting spectral material basis images from public Energy-Integrating Detector (EID) CT images. In the first step, we use a Denoising Diffusion Implicit Model (DDIM) to generate EID CT images from PCCT images. In the second step we use a U-Net with a Domain-Adversarial Neural Network to predict water and iodine maps from generated EID CT images. We also reconstruct basis images and virtual monoenergetic images (VMIs) from the predicted material-basis maps for evaluation. We evaluated the generated water and iodine maps as well as the 40 and 70 keV PCCT images in terms of Hounsfield Unit accuracy, modulation transfer function and noise power spectrum as well as qualitative image appearance. The reconstructed 40 and 70 keV PCCT images exhibit higher spatial resolution while preserving the anatomical structures and textures of the original EID CT images, thereby demonstrating the feasibility of the proposed approach. The proposed framework provides a feasible approach for synthesizing PCCT spectral material-basis images from conventional EID CT without requiring paired images. This method has the potential to provide large sets of synthetic training and evaluation data for PCCT algorithm development in data-limited environments.

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nucl-ex 2026-06-23

Nickel (gamma,p) cross section twice JENDL-5 prediction

by O. Nusair, N. Solomon +2 more

Measurements of Bremsstrahlung-Averaged Cross Sections for Reactions on Natural Nickel Targets at Eendpoint = 40MeV

Bremsstrahlung-averaged value reaches 9.192 mb and matches TALYS-2.2 but not JENDL-5 evaluation.

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Bremsstrahlung activation measurements were performed to study the production of $^{57}\mathrm{Ni}$, $^{56}\mathrm{Ni}$, $^{58}\mathrm{Co}$, $^{57}\mathrm{Co}$, $^{56}\mathrm{Co}$, and $^{55}\mathrm{Co}$ from natural nickel targets irradiated with photons generated by \SI{40}{MeV} electrons incident on a tantalum converter. Bremsstrahlung spectra were modeled using MCNP6.3\texttrademark{} and experimentally validated through activation of natural tin. Bremsstrahlung-averaged cross sections were extracted from end-of-irradiation activities, yielding $\langle\sigma\rangle = 8.983~\pm~0.028$~mb for $^{58}\mathrm{Ni}(\gamma,n)^{57}\mathrm{Ni}$, $0.248~\pm~0.025$~mb for $^{58}\mathrm{Ni}(\gamma,2n)^{56}\mathrm{Ni}$, $0.704~\pm~0.218$~mb for $^{nat}\mathrm{Ni}(\gamma,pxn)^{58}\mathrm{Co}$, $9.192~\pm~0.386$~mb for $^{58}\mathrm{Ni}(\gamma,p)^{57}\mathrm{Co}$, and $2.239~\pm~0.355$~mb for $^{58}\mathrm{Ni}(\gamma,pn)^{56}\mathrm{Co}$. A $90\%$ confidence-level upper limit of $\langle\sigma\rangle < 0.021$~mb is established for the $^{58}\mathrm{Ni}(\gamma,p2n)^{55}\mathrm{Co}$ channel. Comparison with JENDL-5 evaluations and prior studies indicates channel-dependent agreement, with residual discrepancies observed for selected charged-particle emission reactions. In particular, the measured $^{58}\mathrm{Ni}(\gamma,p)^{57}\mathrm{Co}$ cross section exceeds the JENDL-5 prediction by approximately a factor of two, whereas TALYS-2.2 calculations reproduce the experimental value, suggesting an underestimation of the $(\gamma,p)$ channel strength in JENDL-5 under bremsstrahlung conditions. For the $^{58}\mathrm{Ni}(\gamma,pn)^{56}\mathrm{Co}$ channel, both TALYS-2.2 and JENDL-5 predictions are in reasonable agreement, while the present measurement is higher by approximately a factor of two, though with larger associated uncertainty.

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physics.med-ph 2026-06-23

Dual kV tubes differ by over 20% in output at equal settings

by Luka Matej Devenica, Lixiang Guo +2 more

Dosimetric Quantification of a Commercial Dual-Tube kV X-Ray System for Preclinical FLASH Research

Compensation reduces the difference to under 1% and cuts overdose volume in intestinal simulations from over 22% to under 7%

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A kV dual tube system has been disseminated as a commercial research platform for preclinical FLASH radiotherapy (RT). Because the tubes are arranged in a parallel opposed geometry, both output symmetry and time resolved tube synchronization are critical for achieving sufficiently high dose rates (DR) and reproducible study results. We quantified tube output asymmetry observed in depth dose measurements as well as tube synchronization and evaluated their impact on FLASH studies. The dual-tube system defines dose per pulse as the combined single pulse output from both tubes, with DR given by dose per pulse/pulse length. 3D dose distributions were reconstructed from film measurements to assess the impact of output discrepancies. Pulse synchronization between tubes was characterized using a scintillator with 1 ms resolution. We showed >20% discrepancies in output at nominally equal mA/ms settings. After we compensated such discrepancy by decreasing the current of the tube with higher output, the inter tube output difference was reduced to <1%, restoring symmetrical depth dose. We further simulated an in vivo intestinal irradiation in which naive tube settings resulted in >22% of the organ volume receiving >102% of the prescribed dose, compared with <7% when output compensation was applied. We identified a 10.2 +/- 7.0ms synchronization jitter between tubes, which disproportionately impacts the DR at low dose-per-pulse settings, particularly relevant for fractionated studies. Corresponding quality assurance (QA) was designed to monitor tube synchronization over time. We quantified the dosimetric impact of asymmetric output and synchronization and demonstrated implications for preclinical studies. The proposed methodology and QA would mitigate and monitor these effects, ensuring study reproducibility.

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physics.optics 2026-06-23

Projection method finds minimal wavelengths for chromophore inference

by Kevin Scibilia, Marcel Rosier +7 more

A unified perspective on wavelength selection for molecular composition inference from diffuse spectroscopy

Unified framework merges prior techniques with new projection step to satisfy model constraints

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Optical monitoring of living tissue targeting quantification of molecular composition is an active area of research. In the case of broadband spectroscopy, one can attempt to extract molecular, or more precisely, chromophore concentration from a broad-range spectrum of the reflected light. However, selecting the shortest wavelength range sufficient for quantitative optical monitoring remains an open problem. Various wavelength optimization methods are scattered throughout the literature, however, there is no unified view to date. Our work's motivation is to construct a wavelength selection framework by unifying existing selection approaches and propose a novel projection-based method that allows for the pre-identification of a wavelength range that is adequate for the final selection. The framework specifically focuses on proposing different methods that quantify match or mismatch between the chosen light-matter interaction model, defined by the chosen endmembers (e.g. molecular chromophores), and the measured intensity from the spectroscopic data. To evaluate the framework, we perform a retrospective analysis on a broadband spectroscopy dataset of piglets during an induced hypoxia-ischemia state. Overall, we show that our novel projection-based method can be used for band selection, and that existing approaches can be used in conjunction to select an optimal minimal wavelength set that satisfies biophysical model constraints.

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physics.med-ph 2026-06-23

Neural framework corrects PET positron blurring while restoring texture

by Nerea Encina-Baranda, Yifan Zheng +6 more

A Positron Range Correction with Texture Preservation Framework in PET Imaging

PRC-TP reaches 99 percent contrast recovery and 0.997 texture amplitude match to ground truth in 82Rb simulations.

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Positron range (PR) blurring is a fundamental resolution limitation in PET imaging with high-energy positron emitters such as 82Rb, causing contrast loss and spill-out effects across heterogeneous tissue interfaces. We propose PRC-TP, a positron range correction (PRC) framework with explicit texture preservation that decouples deterministic resolution recovery from stochastic texture restoration. A nnFormer-based neural network (NN) was trained on patient-derived Monte Carlo simulations to map PR-degraded 82Rb reconstructions to PR-free references using attenuation maps as anatomical context. However, this NN also significantly removed the noise in the images, which could impact some texture analysis methods or make the images look unrealistic. An auxiliary Noise2Noise model estimates that smoothing effect, enabling texture extraction and transfer to the PR-corrected prediction through Model-consistent Texture Re-Injection (MTRI). In simulated patients, PRC-TP preserved contrast recovery close to ground truth (GT) (98.96-99.04%) while restoring noise and CNR closer to the reference. The function-based MTRI formulation achieved near unity global texture amplitude agreement with GT (0.997 +/- 0.011), reducing the input texture amplitude bias (0.951 +/- 0.011). Radiomics analysis showed improved agreement with GT across texture-sensitive feature families. A clinical 82Rb evaluation showed trends consistent with simulations, including comparable contrast-ratio increase (10.18% vs. 10.99%) and restoration of texture suppressed by PRC. These results support PRC-TP as a practical framework for resolution recovery with acquisition-consistent texture preservation in PET imaging. Submitted to IEEE TRPMS.

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physics.med-ph 2026-06-22

Tomography beams chosen directly from sinogram data

by San Dinh, Zichao Wendy Di +1 more

Adaptive Beam Selection for Efficient Scanning Probe Tomography

Method skips reconstructions by using an acquisition function on raw projections to balance new and known information.

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In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs, acquisition time and the radiation dose imparted to the sample. One mitigation to these trade-offs is to adopt a sequential design of experiments, in which each subsequent measurement is determined as a function of previously acquired data in order to maximize information gain. In practice, a widely used heuristic to maximize information is to align beams with the edges of the sample. A key challenge, however, is that the true sample is unknown, so identifying edge-aligned beams typically requires reconstructing the sample based on available measurements. This work proposes a novel sequential design method that identifies edge-aligned measurements directly from the sinogram, bypassing any reconstruction, thereby improving computational efficiency and reducing the experimental design's susceptibility to reconstruction errors. Our method dynamically selects the next set of measurement beams by maximizing an acquisition function that balances exploration and exploitation over the domain of all possible measurements, improving reconstruction quality while reducing measurement redundancy.

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eess.IV 2026-06-22

Exact VJP gradients keep fidelity in representation-space MRI unrolling

by Efe Il{i}cak, Baris Imre +5 more

Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction

The method back-propagates measurement residuals precisely into learned representations and reports higher quality than heuristic data-consi

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Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems while maintaining interpretability. While most DUNs operate in the object domain (e.g., image space), recent variants explored representation spaces for improved information flow. However, these methods rely on heuristic methods for data consistency (DC), sacrificing fidelity with measurements. In this work, we introduce DUNE (Deep Unrolled Networks in rEpresentation space), a framework that maintains exact adherence to physical measurements while operating in learned representation spaces. By deriving the DC gradient via the chain rule and implementing it through the Vector-Jacobian Product (VJP), we enable exact backpropagation of measurement residuals into the representation space. This formulation supports diverse architectural backbones, including pre-trained encoders to guide the iterative process. We assess DUNE against state-of-the-art baselines on accelerated MRI reconstruction tasks, demonstrating that exact VJP-based gradients yield superior reconstruction quality and structural fidelity across both single-channel portable low-field and multi-channel clinical high-field MRI acquisitions. The code will be available upon publication at https://github.com/EfeIlicak/DUNE.

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physics.med-ph 2026-06-22

OpenPINT turns segmented geometry into MCNP inputs for BNCT planning

by Ian Postuma, Sara J. González +6 more

OpenPINT: Open-source Planning for Isoeffective Nuclear Treatments in BNCT research

The open-source module extracts component-wise dose maps with under 0.13 percent error in fine-resolution phantom tests.

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Objective: Present OpenPINT (Open-source Planning for Isoeffective Nuclear Treatments), an open-source treatment planning system for nuclear therapies that integrates Monte Carlo dose calculations with modular dosimetric and radiobiological models for photon-isoeffective dose evaluation. Approach: We describe the software architecture, implementation choices, and data flow from segmented geometry and source configuration to NIfTI dose outputs. We define BNCT-relevant dosimetric metrics and evaluate the workflow with reproducible analytic and voxelized cylindrical-phantom benchmarks, supplemented by a geometric patient-positioning example. Main results: The module provides a reproducible and scriptable path for generating MCNP-ready inputs, extracting component-wise BNCT dose maps, and computing analysis-ready outputs for quality checks and decision support. Fine-resolution voxelized configurations reproduced the 1 mm analytic reference within 0.13% for the brain-limited irradiation-time endpoint, whereas the full voxelized sweep exposed deviations up to 4.42% in coarse 8--10 mm configurations. Patient-wide gamma pass rates were at least 99.60% for the evaluated mesh/interpolation cases, while low-dose DVH-tail quantities remained sensitive to boundary discretization. Significance: This first paper isolates and validates the simulation-preparation and dosimetric-analysis core of an open-source BNCT treatment-planning platform. It establishes a foundation for subsequent work on optimization, biological weighting, and clinical workflow integration.

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physics.med-ph 2026-06-22

Virtual coil method yields clean phase maps for brain MRI

by Barbara Dymerska, Oliver Josephs +4 more

MORSE-PI -- Flexible and artefact-free image reconstruction for structural and functional QSM and other phase-critical imaging applications

MORSE-PI corrects phase offsets using noise-enhanced coil combinations, reducing artefacts compared to standard methods at 3T and 7T.

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Phase imaging applications such as QSM are highly sensitive to noise amplifications, phase singularities, and other artefacts, particularly in challenging scenarios such as ultra-high field (7T), under-sampled or single-echo acquisitions. We present a novel image reconstruction method, MORSE-PI, designed to produce high-SNR, artefact-free, and singularity-free phase images for both structural and functional phase-based brain imaging. MORSE-PI extends our previous approach, MORSE, by introducing a Virtual Reference Coil (VRC). The VRC is constructed as a linear combination of coil sensitivity maps, with correlations enhanced between coil elements using the noise covariance matrix. Such a VRC ensures robust signal support across the entire brain and is used to correct phase offsets in the MORSE-derived coil sensitivity estimates, resulting in artefact-free, high SNR phase. Compared to GRAPPA with ASPIRE phase correction, MORSE-PI demonstrates greater robustness to artefacts such as noise amplification and aliasing, and shows improved reproducibility in structural imaging at both 3T and 7T. Unlike ESPIRiT and GRAPPA combined with adaptive coil combination methods, MORSE-PI yields singularity-free phase maps. MORSE-PI enables high-SNR reconstructions even for the most challenging scenarios, such as single-echo EPI at 7T. Its efficient, containerised implementation using the Gadgetron framework supports deployment on the MRI scanner console during measurements. MORSE-PI offers a flexible and computationally efficient solution for generating high-SNR, artefact- and singularity-free phase images in both single- and multi-echo GRE and EPI acquisitions. This makes it particularly well-suited for structural and functional QSM, as well as other phase-based MRI applications. Its robustness and rapid computational time facilitate efficient deployment on scanners across field strengths.

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eess.IV 2026-06-22

Clinical metadata lifts TMJ disc segmentation Dice by up to 4.96

by Dayun Ju, Chanyoung Kim +5 more

Anatomically Consistent TMJ Disc Segmentation via Semantic Anchoring and Clinical Priors

TISC uses semantic prototypes from adjacent slices plus mouth-open-limitation data to produce coherent masks on 2,488 MRI volumes.

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Segmenting the temporomandibular joint (TMJ) disc from MRI is essential for accurate diagnosis of internal derangement, yet it remains unreliable in practice due to its small size, low contrast, and morphological variability. Existing methods, primarily adapted from general segmentation architectures, often produce fragmented or anatomically inconsistent masks, leading to unstable measurements of disc position and shape for downstream diagnosis. To address these challenges, we propose TISC, a TMJ disc segmentation framework that integrates semantic anchoring with clinical metadata-guided boundary refinement. The framework first establishes robust disc localization in the foundation model feature space via a Prototypical Semantic Anchoring (PSA) module that aggregates adjacent-slice MedDINOv3 features and derives a prototype-driven similarity map. It then performs targeted boundary refinement through a Clinical-Metadata Point Refinement (C-MPR) module, with point-wise predictions modulated by Mouth Open Limitation (MOL), a clinical indicator associated with disc displacement without reduction. On a large-scale cohort of 2,488 PD MRI volumes from 1,300 patients, our method achieves up to a 4.96 Dice improvement over strong baselines across diverse architectures, delivering more anatomically coherent and clinically reliable TMJ disc segmentation.

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physics.med-ph 2026-06-19

Aggregated spectra enable efficient MSCT reconstruction despite inconsistent geometries

by Ziqiang Zhang, Chong Chen

Efficient and Accurate Image Reconstruction for Geometric-Inconsistent Multispectral CT with Ray-Dependent Energy Spectra

The block-product Jacobian approximation yields an algorithm with convergence guarantees that outperforms prior methods on simulated noisele

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In practical multispectral computed tomography (MSCT), the scanning geometric parameters under different X-ray energy spectra are often inconsistent, and the distributions of the energy spectra are even ray-dependent. However, existing algorithms cannot effectively and accurately solve the associated image reconstruction problem. To address this limitation, using the proposed aggregated energy spectra, we approximate the Jacobian matrix of the nonlinear forward operator at certain special points (e.g., the zero point) as a block product of a diagonal matrix composed of projection matrices and a very small-scale matrix, and then based on this matrix with a special structure, propose an efficient and accurate image reconstruction algorithm tailored for geometric-inconsistent MSCT with ray-dependent energy spectra. Under appropriate conditions, we establish the convergence theory for the proposed algorithm. Furthermore, numerical experiments using both noiseless and noisy projection data are conducted to verify the performance of the proposed algorithm, which demonstrate that the efficiency and accuracy of this algorithm are much higher than existing algorithms, offering the flexibility and scalability to accommodate various MSCT imaging configurations.

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physics.med-ph 2026-06-19

Sex-specific haematocrit barely shifts coronary flow metrics

by C.Shen, M.Zhang +3 more

Physiological Sex-Specific Haematocrit Has Minimal Effect on Coronary Computational Haemodynamics: Modelling Implications for Blood Rheology

Simulations across healthy and stenosed arteries show only tiny differences, so standard rheology models remain sufficient for most studies.

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Haematocrit influences blood viscosity and may affect coronary computational fluid dynamics (CFD). However, previous studies examined broad or pathological haematocrit ranges, and it remains unclear whether female-specific haematocrit variations within the physiological range produce meaningful changes in coronary haemodynamics. 15 female coronaries were analysed, including healthy arteries and diseased models with mild, moderate and severe stenosis. A haematocrit-dependent Carreau-Yasuda model was developed. CFD simulations were performed using the standard rheology model and a female-specific haematocrit-based model (40%). Time-averaged endothelial shear stress (TAESS), ESS gradient (ESSG), temporal shear variation index (TSVI), helicity, and low/high TAESS exposure were compared across coronary trees, arterial segments, bifurcations, stenosed vessels and corresponding narrowed regions. The female-specific model produced statistically significant differences from the standard model across all metrics and coronary regions (p < 0.05). However, the absolute differences were small, indicating a limited haemodynamic impact. Bland-Altman analysis showed narrow biases and limits of agreement. Linear regression demonstrated significant associations between inter-model differences and haemodynamic magnitude for TAESS, ESSG, helicity intensity, and adverse TAESS exposure, but the slopes were small. Similar findings were observed in stenosed arteries, where both models captured comparable flow disturbances across stenosis severities. Female-specific haematocrit variation within the physiological range is computationally detectable but haemodynamically negligible in coronary CFD. A standard rheology model is therefore likely sufficient for most coronary CFD studies, while personalised haematocrit modelling is more relevant for patients with abnormal haematocrit or rheology-focused studies.

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eess.IV 2026-06-19

Contour constraints halve registration error in head-neck specimens

by Qingyun Yang, Jon S. Heiselman +6 more

Contour-Constrained Deformable Registration with Parameter Characterization for Head and Neck Surgical Guidance

Deformable method with boundary penalties cuts target error to 5.62 mm, a 49% improvement over rigid alignment for cancer margin relocation.

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With 890,000 annual new cases globally, head and neck squamous cell carcinoma has one of the highest recurrence rates among solid malignancies. Although frozen section analysis is the standard of care for intraoperative margin assessment, accurately relocating detected positive margins on the resection bed remains challenging due to imprecise alignment between resected specimens and their resection bed, compounded by post-resection mucosal tissue shrinkage. We present a biomechanics-driven deformable registration framework that corrects post-resection tissue deformation to provide intraoperative guidance. Our approach registers 3D specimen meshes to intraoperative resection bed point clouds using a deformable registration approach based on regularized Kelvinlet basis functions. The registration matches surface point clouds, fiducial landmarks, and boundary contour constraints that directly penalize perpendicular distance-to-agreement between specimen and resection bed boundaries. Across nine specimens from skin, buccal mucosa, and tongue sites, the overall mean target registration error was $11.11 \pm 4.07$ mm using rigid registration, which decreased to $8.20 \pm 2.68$ mm (26.19\% reduction) using deformable registration without contour constraint. The proposed contour-constrained deformable registration further reduced the error to $5.62 \pm 2.28$ mm, a 49.41\% reduction relative to rigid registration. We observed the largest reduction in the most clinically challenging tongue specimens. We also performed a systematic two-stage parameter search to characterize the relative importance of surface alignment, fiducial correspondences, contour constraint, and strain energy regularization. This search revealed that contour weighting dominates registration accuracy for tissue types with large lateral deformation, while the algorithm operates over a broad range of parameter combinations.

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eess.IV 2026-06-18

Incentives favor algorithms over concepts in medical imaging AI

by Mark A. Anastasio

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI

Conceptual work that reframes problems and metrics receives less reward than computational tweaks, limiting clinical translation.

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Artificial intelligence has driven rapid progress in medical imaging research, producing increasingly sophisticated algorithms and steady improvements on benchmark tasks. However, this algorithm-centric trajectory has also revealed a growing imbalance: while computational methods advance rapidly, the conceptual foundations that define imaging tasks, evaluation metrics, and clinical meaning sometimes remain underexamined. In this Perspective, we distinguish algorithmic innovation, which focuses on improving computational implementations and performance within a fixed problem definition, from conceptual innovation, which reframes what problems are posed, how success is measured, and why an approach is clinically relevant. We argue that prevailing incentive structures, training pathways, and publication norms disproportionately reward algorithmic novelty, particularly for early-career researchers, while at times undervaluing conceptual contributions that are essential for scientific maturation and clinical translation. Through representative examples from medical imaging AI, we show how insufficient conceptual grounding can lead to misaligned objectives, fragile generalization, and limited real-world impact. We conclude with actionable recommendations for researchers, mentors, reviewers, and journals to better recognize, support, and integrate conceptual innovation alongside algorithmic advances.

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physics.med-ph 2026-06-18

Quantum methods image the retina with far fewer photons

by Mukhit Kulmaganbetov, Dmitry Pushin +7 more

Introduction to Quantum Ophthalmology

Single-photon detection and correlation techniques cut light exposure while preserving diagnostic quality and probing vision limits.

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Quantum technologies are rapidly advancing across multiple research domains, with a growing impact on biomedical imaging and sensing. We examine their emerging role in ophthalmology through four complementary directions: photon-limited retinal imaging, correlation based imaging, nanoscale optical probes, and quantum-limited visual perception. Advances in optical coherence tomography and single-photon detection enable imaging under strict photon budget constraints, reducing phototoxicity while preserving image quality. Correlation-based approaches, including ghost imaging, offer alternative strategies for image formation in low-light and scattering environments, although practical implementation remains limited by detection efficiency and acquisition time. In parallel, nanoscale optical platforms such as quantum dots provide tunable and photostable probes for enhanced contrast and targeted delivery, with ongoing challenges related to biocompatibility and clinical translation. Finally, experiments at the single-photon level and with structured light fields demonstrate how the visual system itself operates near physical detection limits and can be probed using controlled optical states. While many of these approaches remain at an early stage, they collectively illustrate how quantum and quantum-inspired methods may augment current ophthalmic imaging and diagnostic technologies while providing new tools for studying visual function under well-defined physical constraints.

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cond-mat.stat-mech 2026-06-18

Hyperstatistics models Brownian motion and brain dynamics

by Lucas Squillante, Samuel M. Soares +2 more

A few remarks on hyperstatistics and some applications

The framework handles non-Boltzmann-Gibbsian behaviour in physical and biological systems at high accuracy.

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In a recent paper [arXiv:2604.24783 (2026)], we have proposed a general approach to treat systems with inherent non-Boltzmann-Gibbsian behaviour. Given the extremely high accuracy of our approach, we have adopted the term hyperstatistics. We have applied such a statistical mechanics approach, i.e., hyperstatistics, to the discharge of a capacitor in a RC series circuit, pumping of $^4$He of a closed cycle cryostat, midrapidity data of $p$-Pb collisions at the LHC, as well as for the distribution of accelerations in turbulent systems. Here, we discuss into more details the ground of hyperstatistics. We demonstrate the versatility of hyperstatistics upon applying it to the velocity autocorrelation function in Brownian motion and also regarding its potential to describe brain dynamics.

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physics.med-ph 2026-06-18

Hybrid OCT-PAT method raises strain SNR and stiffness accuracy on phantoms

by Ekaterina Sherina, Lisa Krainz +2 more

Quantitative Multi-Modal Optical Coherence Photoacoustic Elastography

Merging complementary absorption-scattering data outperforms single-modality elastography in controlled tests

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We present a novel multi-modal optical coherence photoacoustic elastography (OCPE) framework, which combines two imaging modalities, optical coherence tomography (OCT) and photoacoustic tomography (PAT), to enable complementary absorption-scattering measurements for the extraction of quantitative tissue features via quasi-static elastography. For this, we develop a sophisticated hybrid inversion algorithm for merging the complementary information layers contained in both OCT and PAT-based elastography measurements, and perform systematic evaluations to assess the impact of hybrid elastography data on strain and stiffness reconstructions. Studies on a silicone elastomer phantom demonstrate that the combined OCT-PAT approach outperforms single-modality OCT elastography and PAT elastography, yielding higher strain signal-to-noise ratio and improved stiffness estimates. These results establish the advantage of multi-modal complementary imaging and data merging for accurate, high-resolution elastographic strain and stiffness mapping in both scattering and absorbing materials.

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eess.IV 2026-06-18

Extremum seeking cuts registration cost 3.5-fold for ultrasound microscopy

by Jipeng Yan, Chang Liu +5 more

Efficient Image Registration for Ultrasound Localization Microscopy by Obtaining Gradients via Integration Across Iterations

Perturbed similarity metrics integrated over iterations replace explicit gradients, enabling 219 micrometer resolution below the diffraction

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Tissue motion correction through image registration is essential for ultrasound localization microscopy (ULM). Parametric image registration is commonly formulated as an optimization problem where motion parameters are iteratively updated to maximize image similarity, and used optimization algorithms typically rely on gradient information, the explicit evaluation of which can become computationally demanding. This work investigates Extremum Seeking Control (ESC) as an alternative to explicit derivative evaluation in image registration. By obtaining descent information via integrating perturbed and demodulated image similarity metric across iterations, ESC avoids differentiation of the image similarity metric with respect to motion parameters in each iteration. The classical ESC, whose optimization behavior approximates that of classical gradient descent (GD), is first compared with GD for affine image registration using simulated ground-truth motions derived from a beating ex vivo porcine heart dataset. The results show that ESC achieves registration accuracy and convergence behavior comparable to GD while reducing per-iteration computational cost by approximately 3.5-fold. ESC is subsequently employed in a two-stage motion correction pipeline, where affine registration compensates for global tissue motion and B-spline registration corrects residual local deformation. The proposed method is applied to ULM imaging of a beating ex vivo porcine heart and achieves a spatial resolution of 219 um, substantially below the half-wavelength diffraction limit of 321 um associated with 2.4 MHz diverging-wave imaging. These results demonstrate that ESC provides an effective alternative to explicit derivative evaluation in ULM image registration, enabling accurate motion correction and high-quality super-resolution imaging.

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physics.med-ph 2026-06-17

Prototype PET scanner yields first zebrafish brain images

by Steven Seeger, Hong Phuc Vo +7 more

MERMAID-v1 PET Scanner Prototype: Initial Characterization and First Zebrafish Scans

Two-head setup reaches 0.7 mm resolution and detects organ uptake in living fish despite missing corrections.

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MERMAID-v1 is a prototype PET scanner designed to support biomedical research involving adult zebrafish and similar species. The current experimental setup has been characterized, and scans of various phantoms, as well as adult zebrafish have been conducted. A dedicated reconstruction software was implemented, including accurate modeling of the parallax effect. The average energy resolution was 21.6% (FWHM at 511keV), with no significant dead-time effects observed for activities up to 18MBq. The absolute sensitivity at the center of the field of view (FOV) ranged from 0.06% to 0.31%, depending on the energy window (from 450-550 to 300-600keV), reflecting the limitations of the current two-head configuration. In the central 12mm of the transaxial FOV, the averaged spatial resolution is approximately 0.77mm (FWHM) transaxially and 0.66mm axially, as evaluated using a point source. Image quality was assessed using a downscaled NEMA-inspired IQ phantom and a 3D-printed Derenzo phantom. The reconstructed images suggest a spatial resolution around 0.7mm - 0.8mm, despite the lack of depth-of-interaction information. The first ex- and in-vivo PET scans of adult zebrafish were successfully performed, showing detectable tracer uptake in organs such as the brain and eyes despite low initial activity levels. These results confirm MERMAID-v1 capability to obtain useful results from the acquired data from living, anesthetized fish in a water-filled imaging chamber. While no scatter, attenuation, or efficiency corrections have yet been implemented, this work establishes a working proof-of-concept for dedicated PET imaging of small aquatic vertebrates. Future developments will focus on developing correction techniques, expanding the detector array, and integrating complementary modalities such as CT.

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physics.med-ph 2026-06-16

Lumped model predicts subglottal pressures during vocalization

by Maruf Md Ikram, Maryam Naghibolhosseini +1 more

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics

Piston-cylinder lung and airway representation, driven by deep-learned vocal fold motion, yields first respiratory simulation for phonation.

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A predictive physics-based model of human respiratory, phonatory, and articulatory subsystems is developed to simulate voice production. Representing lungs, compressible airways, and vocal folds as spring-damper-mass controlled piston-cylinder systems, our mathematical model robustly captures the intricate dynamics of airways during phonation. The nonlinear viscoelastic properties of lung tissues and compressible airways were investigated, yielding a responsive and expressive baseline respiratory model with the capability to further extend into a patient-specific model for both respiration and phonation. The resulting framework was subsequently integrated with a mechanical representation of the vocal tract, governed by the glottal area waveform (GAW) capturing the motion of vocal folds during sustained phonation. The GAW is extracted from laryngeal high-speed videoendoscopy data of a normophonic participant using deep learning. Our novel paradigm transcends beyond modeling the respiratory system, enabling AI-driven modeling of vocalization, including vocal fold dynamics, interactions with flow aerodynamics, and flow resistances, induced by the oscillatory behavior of vocal folds. Our investigation leads to the first-ever simulation of respiratory dynamics for vocalization, directly advancing the prediction of subglottal pressure distributions, impossible to measure directly and noninvasively in humans, dynamic resistances, and energy transfer mechanisms during phonation in voice mechanics.

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physics.med-ph 2026-06-12

Beam-membrane model adds vocal fold posturing at low cost

by Mohamed A. Serry, Matías Zañartu +1 more

A beam--membrane biomechanical vocal fold model incorporating posturing and glottal conformation

Boundary moments from muscle activation let the reduced model reproduce glottal closure patterns seen in detailed simulations while running

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The posture of the vocal folds produced by laryngeal muscle activation plays a central role in determining the dynamics of voice production. Abnormal vocal fold configurations are frequently associated with inefficient phonation and a variety of voice disorders. Although diverse glottal closure patterns have been observed clinically, the biomechanical mechanisms governing their dynamic behavior and resulting phonatory characteristics remain incompletely understood. Moreover, existing numerical models that incorporate the effects of the intrinsic musculature on posturing and glottal conformation are computationally expensive, which limits their suitability for large-scale parametric investigations. In this work, we introduce a computationally inexpensive vocal fold (VF) model wherein the body and cover VF layers are treated as a composite beam and a coupled membrane, respectively. Intrinsic laryngeal muscle activation, in addition to positioning the arytenoid cartilages and cricothyroid joint, introduces moments at the boundaries of the structure that influence glottal conformation. The model produces phonatory characteristics that are qualitatively consistent with those reported in high-fidelity finite-element models and clinical studies, thereby supporting its predictive capability while offering substantial computational advantage. The proposed framework provides biomechanical insights into the influence of incomplete glottal closure on phonation dynamics and may serve as a computationally tractable tool for investigating mechanisms underlying certain voice disorders.

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cs.CV 2026-06-12

Dual-domain equivariant GAN boosts CT-PET synthesis accuracy

by Gabriel Steele, Alzahra Altalib +1 more

Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis

Joint spatial-frequency learning with rotation consistency yields higher fidelity images than standard GAN baselines on HECKTOR data.

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We present a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) for multimodal CT-PET image synthesis. Traditional GAN-based approaches often operate solely in the spatial domain and ignore geometric consistency, resulting in limited structural fidelity. DDE-GAN addresses these challenges by jointly learning from both spatial and frequency (Fourier) domains, capturing complementary anatomical and spectral information. Furthermore, rotational equivariance embedded in the physics of the CT and PET measurements are integrated into the loss of both the generator and discriminator to ensure consistent responses under rotations, improving anatomical accuracy. A hierarchical dual-domain training strategy enforces intra- and inter-domain consistency through multi-stage loss functions. Evaluated on the HECKTOR 2022 CT-PET dataset, DDE-GAN achieves superior synthesis quality over baseline models for CT-PET image synthesis. The results demonstrate that combining dual-domain learning with geometric equivariance substantially enhances multimodal image synthesis accuracy and robustness, enabling practical applications in PET completion and data augmentation.

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physics.med-ph 2026-06-12

Three GPU tweaks reach kilohertz 3D ultrasound rates

by Luuk Verhoef, Pieter Kruizinga

ffdas: Volumetric ultrasound reconstruction at warp speed

Memory alignment, mixed precision and tensor cores let 1024-element arrays reconstruct 128-cubed volumes in real time

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Volumetric ultrafast ultrasound imaging demands reconstruction of images with millions of voxels thousands of times per second, creating computational challenges that limit both real-time feedback and easy offline analysis. Graphics processing units (GPUs) are well suited to this workload, yet we show that standard delay-and-sum implementations underutilize GPU resources through fragmented memory access patterns, even when sufficient computational capacity is available. Three optimization strategies address this: aligning memory access with GPU transfer granularity, halving memory traffic through mixed-precision storage, and exploiting spatial locality to utilize tensor core arithmetic. Together, these achieve kilohertz frame rates for $128^3$-voxel grids with 1024-element arrays, substantially outperforming existing implementations while maintaining image quality. This enables real-time volumetric imaging at scales previously restricted to offline processing, supporting applications such as intraoperative brain imaging and brain-computer interfaces where immediate feedback is essential. We release our implementation as part of the open-source \texttt{ffdas} library.

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physics.med-ph 2026-06-12

Radiology semantics plus lab biomarkers sharpen lung cancer survival forecasts

by Jingxiang Shi, Yiming Wang +7 more

Radiology-Report Semantic Modelling and Host-Response Laboratory Biomarkers for Multimodal Survival Prediction in Lung Cancer

AMRS reaches C-index 0.92/0.85 and splits outcomes inside TNM stages in a 574-patient two-center cohort.

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TNM staging is essential for lung cancer management, but patients within the same anatomic stage often show heterogeneous survival outcomes. We developed a multimodal adaptive risk score (AMRS) that integrates radiology-report semantics with routinely available clinical laboratory biomarkers. In a retrospective two-center cohort, 1129 patients diagnosed between December 2017 and February 2026 were screened; 574 patients were included after exclusion for short follow-up or missing imaging reports and were split into training (n = 459) and test (n = 115) cohorts. Radiology reports were encoded with a domain-adapted MC-BERT branch to capture imaging-derived semantic information, while clinical and laboratory variables were modeled after Mahalanobis-distance-based imputation using random survival forests. Weighted risk fusion generated the final patient-level score. AMRS achieved C-index values of 0.920 in training and 0.849 in testing, and separated survival trajectories across clinical subgroups and TNM-related strata. SHAP analysis identified hematologic, inflammatory, coagulation, nutritional, tumor-marker, organ-function, and age-related contributors. AMRS may complement TNM staging in imaging-centered oncology workflows, but prospective validation, calibration, ablation testing, and clinical-utility assessment are required before deployment.

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eess.IV 2026-06-12

Kernel alone flips AI lung nodule size category in 5% of cases

by Daniel Soliman

Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata

Noise instead reduces detection for small nodules; both effects stay invisible to DICOM tags.

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AI governance for medical imaging is formalizing: the 2026 ACR-SIIM Practice Parameter recommends local acceptance testing and ongoing drift monitoring, and the ACR Assess-AI registry monitors AI outputs using DICOM metadata for context. We argue that a necessary, currently unmonitored layer sits beneath output metrics: whether incoming studies remain within the acquisition envelope a model was validated on. Using a LUNA16-trained MONAI RetinaNet lung-nodule detector, we test whether acquisition state behaves as a structured, measurable variable. On real paired CT differing only in reconstruction kernel (NLST B30f vs B80f), kernel alone shifted AI-measured diameter and flipped a Fleischner size category in 5.2% (8 of 155) of nodules at fixed patient and acquisition, while detection confidence was unchanged (Wilcoxon p=0.22). Under controlled LIDC-IDRI perturbations the effects dissociated by axis: the noise axis degraded detection confidence (p=5.9e-32, concentrated in nodules under 6 mm) but not measurement, while the frequency/kernel axis corrupted measurement (p=8.6e-13) but not detection. A 4-feature pixel fingerprint recovered reconstruction identity (patient-level AUC about 0.95 on real CT, 0.995 on a QIBA phantom) where the ConvolutionKernel DICOM tag was uninformative (identical labels across reconstructions). The kernel axis transported across four manufacturers (leave-one-vendor-out AUC 0.94-0.98, matching the within-vendor ceiling). Acquisition state thus maps to distinct AI failure modes, frequency content to measurement reliability and noise to detection sensitivity, and is not recoverable from metadata. Acquisition-aware, input-side validation is the missing layer for the acceptance-testing and drift-monitoring requirements now entering imaging-AI accreditation.

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physics.med-ph 2026-06-11

Double-Gaussian beam model refines proton phase space accuracy

by Qianxia Wang, Poenisch Falk +9 more

Beam modelling of Hitachi PROBEAT proton therapy system for a GPU-based Fast Monte Carlo dose engine

New distributions for particle space and angle aim to improve inputs to GPU Monte Carlo dose calculations for independent QA.

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Background: An in-house dose engine independent of clinic TPS is not only a reliable tool for patient QA verification. More importantly, it plays vital role in cutting-edge research due to its flexibility in implementing new functions. In this study, we upgraded our existing beam model with using double-Gaussian distributions for both spatial and opening angle distributions of particles to obtain more accurate phase space files. It is expected to potentially improve the performance of this independent dose engine in both clinic and research at the expanded MD Anderson proton center.

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physics.med-ph 2026-06-10

Model converts CT and dose into proton spot maps

by Xueyan Tang, Hok Wan Chan Tseung +7 more

A Two-Stage Framework for Fast Proton Spot Map Generation in Pencil Beam Scanning Prostate SBRT Planning

GenSpot produces deliverable plans whose Monte Carlo doses match clinical prostate SBRT within 1 Gy on DVH metrics.

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Background: In pencil beam scanning (PBS) proton therapy, plans are delivered as proton spot maps (PSMs). Although deep learning can rapidly predict 3D dose, direct conversion of dose into deliverable spot patterns remains limited. Purpose: We developed GenSpot, a two stage framework that infers deliverable PSMs from CT and dose, and evaluated it in prostate SBRT by comparing Monte Carlo (MC) doses from GenSpot and clinical PSMs. Methods: GenSpot uses a physics informed projected proton spot map (PrPSM) representation, projecting spots through CT with water equivalent thickness and PDD information to align spots with the CT/dose grid while preserving linearity with spot weights. The dataset included 1,036 fields from 259 prostate SBRT plans, split 80%/10%/10% for training, validation, and testing. A 3D SwinUNETR predicted PrPSMs from CT and dose. Field specific PSMs were reconstructed using column wise nonnegative Lasso regression with precomputed PDD curves. GenSpot and clinical MC doses were compared using MAE, 3D gamma analysis, and composite plan DVH metrics. Results: On the test set, SwinUNETR achieved PrPSM MAE of 0.06 +/- 0.02 with high similarity to clinical PrPSMs. GenSpot MC doses showed low MAE of 0.07 +/- 0.03 Gy in the nonzero dose region and gamma passing rates of 0.90 at the field level and 0.97 at plan level. Composite DVH differences were within 1 Gy for targets and organs at risk, though the CTV showed a modest high dose increase. Spot complexity was similar to clinical plans, with slightly more spots. Prediction and reconstruction averaged 0.02 s and 2.1 s/field. Conclusions: GenSpot generated machine deliverable PSMs from CT and dose whose MC doses closely matched clinical PSM doses in a single institution prostate SBRT cohort. This physics informed dose to spots framework may support automated PBS planning and adaptive replanning, pending broader validation.

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physics.med-ph 2026-06-10

Delivered proton doses deviate from plans in tongue cancer cases

by Qianxia Wang, Edgar Gelover Reyes +10 more

Planned, delivered and variable RBE dose difference analysis for a patient cohort with base-of-tongue cancer treated with IMPT

Anatomy shifts over treatment and variable biological effectiveness change how plans are assessed for head and neck patients.

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Background: To our knowledge, no tools have been installed in clinic for delivered and planned dose differences evaluation. This difference could be large for head and neck patients who suffer the most anatomy changes compared with other treatment sites due to long treatment courses and difficulty in eating. At the same time, variable RBE dose is an increasing concern for proton therapy. The constant RBE 1.1 is widely applied in clinics, however, the real RBE is larger than 1.1 especially at the end of beam range. How they are different and what the influence on plan evaluation are an interesting topic to investigate.

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physics.med-ph 2026-06-10

Calibration fits relaxation parameters for varying tissue attenuation

by Masashi Sode, Gianmarco Pinton

Spatially heterogeneous power-law attenuation with multiple relaxation mechanisms for ultrasound modeling

Method keeps mean errors under 3 percent from 1-20 MHz and reflections below -50 dB in heterogeneous body models.

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Objective. The soft tissue attenuation laws have a magnitude and frequency dependence that varies across tissue types and generally follow power laws. An accurate model of ultrasound propagation in the human body thus may require spatially heterogeneous power-law attenuation alpha(x,f) = alpha_0(x) f^(y(x)). However, a spatially heterogeneous representation of frequency-dependent attenuation is technically challenging, so existing methods introduce simplifying assumptions. For example, prior approaches such as Fullwave 2 achieved <5% error for individual tissue types but required manual parameter tuning for each (alpha_0, y) pair, limiting the construction of realistic tissue libraries. Approach. We introduce a calibration framework that uses derivative-free optimization to systematically fit relaxation parameters across diverse tissue combinations spanning alpha_0 = 0.0022-1.0 dB/(MHz^y cm) and y = 0.4-2.0. The Nelder-Mead algorithm minimizes complex-wavenumber mismatch. The attenuation is extended to a convolutional perfectly matched layer, where the same relaxation formulation is used in the boundaries. Main results. The method achieves mean errors below 3% over 1-20 MHz with dispersion error of 1.1 +/- 0.8 m/s across the clinically relevant core region (y = 0.7-1.4). Boundary reflections remain below -50 dB for clinically relevant tissue exponents (y <= 1.5). We validated the method with two-layer muscle/fat/liver models and confirmed per-layer accuracy (<2.5% normalized error). A 3D abdominal simulation using the Visible Human dataset demonstrates stable propagation with voxel-level heterogeneity in both alpha_0(x) and y(x). Significance. The open-source multi-GPU implementation (Fullwave 2.5) provides a practical tool for patient-specific therapy planning, training data generation, estimation of acoustic radiation force, quantitative imaging, and inverse problem applications.

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physics.med-ph 2026-06-10

Mammography dose varies up to threefold with glandular tissue depth

by Lacey L. Medlock, Murtuza S Taqi +2 more

Range of Normalized Glandular Dose for Mammography Using Patient-Specific Glandular Fractions

A single projection yields patient-specific DgN bounds by repositioning the same glandular map at top, center, or bottom depths.

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Breast cancer is the most common cancer among women, and mammography remains the primary modality for early detection. Because mammography uses ionizing radiation, accurate estimation of normalized glandular dose (DgN) is important for risk assessment. Recent breast dosimetry models, including TG-282, incorporate population-based glandular tissue distributions; however, patient-specific glandular distributions remain unknown from conventional mammographic projections. In previous work (Smith, Dey et al., 2025), glandular fraction (GF) maps were estimated from a single mammographic projection. While these maps determine glandular path length along each projection ray, they do not uniquely define glandular tissue depth. In this work, we propose a framework for estimating a patient-specific range of DgN from a projection-derived GF map. Using simulated data, glandular tissue was distributed to the top, center, and bottom of the breast volume using Siddon ray-tracing. These configurations preserved the GF map while producing maximum, intermediate, and minimum DgN values. Monte Carlo simulations were performed, and DgN was normalized to entrance air kerma. DgN varied by up to a factor of three solely due to differences in glandular tissue depth, despite identical GF maps and visually indistinguishable projection images. Using randomized realizations derived from TG-282 glandular distributions for Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) views, dose ratios were calculated relative to central glandular placement. Central placement overestimated DgN by less than 5 and 15 percent on average for MLO and CC distributions respectively, whereas centroid-based placement underestimated dose by up to 25 percent. These results indicate that patient-specific bounds on DgN can be estimated from limited mammographic information and that central placement provides a conservative dose estimate.

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physics.med-ph 2026-06-10

Stomach motility controls pathogen survival more than average acid levels

by Sharun Kuhar, Jung-Hee Seo +1 more

Flow-Mediated Regulation of Pathogen Survival in the Human Stomach

Hypomotile cases keep nearly 50% of pathogens alive at six minutes versus under 30% with normal motility.

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Foodborne diseases remain a major public-health burden, and the gastric acid barrier serves as the body's primary chemical defense against ingested microbes. Yet experimentally investigating pathogen survival within this environment is highly challenging. Although recent computational stomach models have provided insights into gastric disorders, none have coupled fluid flow, acid transport, and pathogen population kinetics in a realistic stomach to assess gastric acid barrier function. Here, we develop an imaging-based stomach model that tracks 10,000 massless particles representing pathogen colonies ingested with a liquid meal as they are advected through a dynamic, spatially heterogeneous pH field. The model incorporates acid secretion, peristaltic mixing, and gastric tone-driven emptying. Using this framework, we quantify how hypomotility and altered gastric tone influence pathogen survival. Motility emerges as the dominant factor governing pathogen fate. The hypomotile stomach exhibits weaker mixing, retaining nearly 50% of the initial pathogen population alive 6 minutes after ingestion, compared with less than 30% in healthy cases. It also produces broader acid-dose distributions and more heterogeneous survival outcomes. Counterintuitively, among healthy-motility cases, increased gastric tone delivers the highest concentration of viable pathogens into the duodenum, revealing a trade-off between transport and acid-mediated inactivation. These findings demonstrate that conventional metrics such as average pH or gastric emptying rate are insufficient for assessing gastric sterilization. Instead, the present flow-transport-kinetics framework provides new mechanistic insights into pathogen survival and gastric infection risk.

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cs.CV 2026-06-10

DD-INR recovers BOLD signals from accelerated fMRI scans

by Qiaoxin Li (MIND), Caini Pan (NEUROSPIN +5 more

DD-INR: Dynamics-Driven Implicit Neural Representation for Accelerated Whole-Brain Functional MRI Reconstruction

Representing only the time-varying brain changes with an implicit network improves image quality and activation detection in undersampled wh

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Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging with high k-space undersampling: Task-evoked BOLD signals are small in magnitude, which traditional anatomical MRI reconstruction methods fail to recover, as they favor spatial accuracy over temporal fidelity. We present DD-INR, a Dynamics-Driven Implicit Neural Representation framework tailored for accelerated fMRI that benefits from incoherent time-varying sampling and a tailored spatiotemporal prior, outperforming traditional methods, demonstrated in simulation and in-vivo acquisition, both in terms of image quality and retrieval of activation patterns. DD-INR achieves this by splitting the fMRI data into a static background and a temporally varying dynamic component, representing only the dynamics with a dedicated INR, thereby focusing the model's capacity on activation-relevant changes while remaining compact. In general, DD-INR provides a promising framework for accelerated fMRI reconstruction, with the potential to improve the sensitivity and robustness of fMRI studies within practical scan time limits. The source code is available at https://github.com/JoosenLi/DD-INR.

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cs.CV 2026-06-10

Pipeline classifies bone marrow cells into AML diagnostic ratios

by Yuqi Ma, Tianyi Wang +7 more

Patient-Level Diagnosis of Acute Myeloid Leukemia via Deep Learning Analysis of Bone Marrow Smear

YOLO and EfficientNet model detects cells and aggregates CBLC ratios, reaching weighted F1 above 0.86 on external centers.

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Bone marrow smear review remains important for acute myeloid leukemia (AML) assessment, but manual single-cell interpretation is labor-intensive and patient-level diagnosis requires aggregation of many cellular observations. We present a cell-to-patient deep learning pipeline for AML-assisted diagnosis from bone marrow smear images. The study included 258 patients from six anonymized centers, including a main cohort of 169 patients from Centers 1-3 and an external validation cohort of 89 patients from Centers 4-6. A 16-category cell annotation vocabulary was used to describe the global cellular composition, including granulocytic, monocytic, erythroid, lymphoid, eosinophilic, and other cells. Rather than identifying strict AML blasts or leukemic blasts, the model targets an expert-defined composite category termed Composite Blast-like Cells (CBLC), comprising N, N1, M, M1, R, R1, J, and J1 according to the project-wide morphological standard. A fixed YOLO-based segmentation module detected cells, predicted contours were matched to expert polygon annotations by contour IoU, and standardized single-cell crops were generated. An EfficientNet-B0 classifier was trained through a two-stage GT-to-YOLO and YOLO-to-YOLO strategy with class-imbalance correction, center-border regularization, and morphology-assisted supervision. Cell-level predictions were aggregated into patient-level CBLC ratios for AML-oriented diagnostic support. The pipeline achieved stable internal validation and maintained external generalization, with ensemble weighted F1-scores of 0.9076, 0.8696, and 0.9124 on Centers 4, 5, and 6, respectively.

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physics.med-ph 2026-06-10

Local high-resolution seeds enable full-field CT super-resolution

by Wenxin Mo, Yingxian Xia +4 more

Foveated-Imaging Geometry CT Architecture and Seeded Diffusion Model Enabling Global Super-Resolution Reconstruction

Foveated detector geometry plus seeded diffusion produces globally consistent high-resolution volumes from mostly low-resolution data.

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For X-ray computed tomography (CT), a smaller detector pixel size generally leads to higher scanner spatial resolution, but inevitably increases system cost as well as data overhead in acquisition and processing. To achieve high-resolution (HR) CT imaging in a more resource-efficient manner, we propose a Foveated-Imaging Geometry CT (FIGCT) architecture, which integrates local HR data into an acquisition scheme dominated by low-resolution (LR) measurements. We further develop a Diffusion Probabilistic FIGCT Super-Resolution Reconstruction (DPFSR) framework to generate global HR CT images over the full field of view (FOV). The concept of FIGCT is first established, and its typical configurations are characterized according to the arrangement of HR data. Two key indices, namely the HR data fraction (HDF) and the LR-to-HR detector pixel size ratio (LHR), are introduced to describe the FIGCT geometry. The proposed DPFSR incorporates local HR information into intermediate clean-image estimates in both the projection and image domains during the reverse diffusion process. This additional step not only guides HR image generation from LR data, but also improves data consistency between the clean-image estimates and the originally measured data. Preliminary numerical simulation results on FIGCT show that the proposed architecture provides high-precision CT images within the region of interest (ROI) corresponding to the HR data, while the spatial resolution deteriorates rapidly outside the ROI. With DPFSR, global HR reconstruction is achieved on the AAPM Grand Challenge dataset and swine lung CT data, outperforming existing SR methods in terms of Learned Perceptual Image Patch Similarity (LPIPS), PSNR, and SSIM.

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physics.med-ph 2026-06-09

Skull models match beam shapes but err 20-77% on pressure and intensity

by Han Li, Aidan J Horner +2 more

Experimental Validation of Skull Acoustic Modelling Strategies for Transcranial Focused Ultrasound Simulation: A Cross-Comparison Study

Tests inside real skulls show simulations overestimate transmitted exposure and shift the focus by millimetres.

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Accurate acoustic modelling of the skull is essential for simulation-guided transcranial focused ultrasound (tFUS), but commonly used skull parameterisation strategies differ in complexity and reported accuracy. This study experimentally compared five k-Wave skull models: two voxel-wise linear mapping models, two three-layer models, and one single-layer fixed-parameter model. Nineteen regions of interest from five historical and two Thiel-embalmed human skulls were tested at 220 kHz, 680 kHz, and 1000 kHz. Bowl-surface source fields were reconstructed using acoustic holography, and simulated intracranial pressure fields were benchmarked against needle-hydrophone measurements. Across frequencies, mean peak-pressure errors ranged from 20% to 31%, whereas intensity errors reached 41% to 77%. Errors in -6 dB focal volume ranged from 11% to 67%, and focal-position discrepancies were typically several millimetres. Simulations generally predicted smaller insertion losses than measured, indicating a tendency to underestimate skull-related attenuation and overestimate transmitted intracranial exposure. The linear mapping model with fixed attenuation gave the lowest frequency-averaged pressure error, but no model showed a consistent advantage across all metrics. These results show that current skull models can reproduce gross intracranial beam patterns while retaining substantial quantitative uncertainty in exposure, focal coverage, and target localisation.

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q-bio.QM 2026-06-09

Pack count and timing adjustment shifts 35% of medication trajectories

by Cécile Chouquet, Anna-Belle Beau +4 more

Adjusted trajectory of medication exposure taking into account the periodicity of dispensations and the number of dispensed packs and comparative analysis on EFEMERIS database

On EFEMERIS data, the change improves clustering quality and strengthens links to neonatal pathology in high-dose psychotropic groups.

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We presented an adjustment method for the calculation of medication exposure trajectories based on the number of dispensed packs and the type of dispensations (occasional or regular). A comparative study based on the EFEMERIS data was carried out using three different scenarios of trajectory calculation depending on whether or not the number of packs and the periodicity of medication dispensations were taken into account. The impact of the scenario was highlighted using global indicators on the number of Define-Daily Dose (DDD) on all women exposed; the study of changes in individual trajectories from one scenario to another was carried out; we also compared the results of a clustering into four groups. If 65% of the trajectories remained unchanged, we could observe on the rest significant changes in number of DDD and/or on individual exposure profile. We observed 4% of trajectories that were attributed to a different cluster, and the clustering was of better quality with the adjustment method. Depending on the study context, an impact on cluster distribution could be observed for some maternal characteristics and neonatal outcomes. This was the case for a higher occurrence of neonatal pathology for neonates from mothers belonging to the cluster with high doses of psychotropics, thus reinforcing the conclusions of previous studies of a link between high exposure to psychotropic medications and presence of pathology for the newborn.

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cs.CV 2026-06-09

Certainty maps turn DIR uncertainty into dose bounds

by Stefan Heldmann, Sven Kuckertz +5 more

A practical probabilistic framework for deformable image registration uncertainty in radiotherapy dose propagation

A lightweight model treats voxel mappings as random variables to generate probability distributions, expected doses, and DVH envelopes witho

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Deformable image registration (DIR) is widely used in radiotherapy for dose propagation and accumulation, but uncertainty in the underlying deformation can substantially affect clinically relevant dose estimates. We present a practical probabilistic framework for propagating DIR uncertainty to voxel-wise dose statistics and dose-volume histograms (DVHs). The method models the mapped correspondence at each voxel as a random variable governed by a transparent local certainty map that can be defined by simple safety margins, structure-boundary mismatch, or structure-wise conservative uncertainty values. This yields interpretable quantities such as dose probabilities, expected dose, confidence bounds, and induced DVH envelopes. The framework is designed to remain lightweight and interpretable: it avoids complex biomechanical or ensemble-based uncertainty models and instead emphasizes simple parameterization, computational feasibility, and transparent dose metrics. We further introduce a structure-guided in/out strategy as an optional refinement that restricts mapping probabilities to anatomically plausible target regions. The approach is demonstrated on a prostate radiotherapy case study and used to compare different certainty-map strategies and probability kernels. The experiments show that the certainty-map design has a stronger effect on resulting dose and DVH uncertainty bounds than the specific kernel choice, while the additional benefit of the in/out strategy is case-dependent and modest in the present example. Overall, the proposed framework provides a transparent way to incorporate DIR uncertainty into radiotherapy dose assessment and to study how modelling choices affect propagated dose metrics.

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physics.med-ph 2026-06-08

This paper simulates IMPULSED diffusion MRI signals to identify which cellular parameters…

by Wen Li, Yan Dai +4 more

Investigating the Uncertainty of Cellular Microenvironment Parameter Estimations via Diffusion MRI Cytometry

Simulations identify d, Vin, and Dex as low-uncertainty parameters with neural network mapping validated in cell lines for potential tumor m

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This study aims to identify cell microenvironment parameters that can be robustly estimated from IMPULSED diffusion MRI signals and to develop a reliable mapping-based estimation framework. Diffusion MRI signals were simulated using the established IMPULSED model with one pulsed gradient spin echo sequence and two oscillating gradient spin echo sequences at different frequencies. Five cellular parameters were considered: cell diameter ($d$), intracellular diffusion coefficient ($D_{in}$), intracellular volume fraction ($V_{in}$), extracellular diffusion coefficient ($D_{ex}$), and the frequency-dependent slope of $D_{ex}$ ($\beta_{ex}$). Parameter uncertainty was quantified using Jacobian-based sensitivity analysis at an SNR of 30, representing clinically achievable conditions on a 1.5T MRI scanner. To enable direct parameter mapping, signals were logarithmically transformed, reduced in dimension using principal component analysis, and then used to estimate parameters with linear regression, fourth-order polynomial regression, and a fully connected four-layer neural network. Model validation was performed in vitro using MC38 cell lines. Uncertainty analysis identified $d$, $V_{in}$, and $D_{ex}$ as robustly derivable parameters, each with relative uncertainty below 1.0. Among the tested models, the four-layer neural network performed best, with mean absolute errors of 1.7 $\mu$m for $d$, 5.06% for $V_{in}$, and 0.28 $\mu$m$^2$/ms for $D_{ex}$. In vitro validation showed a 6.7% error in cell diameter estimation. These results demonstrate that IMPULSED dMRI can support robust estimation of key cell microenvironment parameters and provide a practical framework for noninvasive assessment of tumor microenvironment changes during radiation therapy response monitoring.

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physics.med-ph 2026-06-08

Sensor in TMS coil reports contact point and angle live

by B. Seyed, M. Koehler +1 more

Coil-Integrated Alignment Sensor for Real-Time Feedback of Coil-Scalp Contact Point and Angle During Transcranial Magnetic Stimulation (TMS)

Low-cost consumer electronics design survives pulses without reset or degradation and gives operators immediate alignment data.

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Whereas coil positioning in transcranial magnetic stimulation (TMS) to reach a specific cortical target with modern focal stimulation coils has been intensively studied, the alignment and contact of a coil with the head is often ignored. Focal figure-of-eight coils have a point on the surface, where they generate the largest induced electric field. This point should touch the head first, and the coil should be approximately tangential to the head in this point. Previous research has demonstrated the large impact if the coil does not touch the head with the right point and that many operators struggle with establishing or maintaining the correct coil-scalp alignment. This paper presents a technological support technology that can monitor the exact position of the contact point and also pressure to provide feedback to users. As the system uses exclusively components from consumer electronics, the sensor is low-cost and affordable. Through proper design, we achieved sufficient robustness so that the sensor does neither reset during TMS pulses and also not show any detectable degradation.

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physics.med-ph 2026-06-08

GRASP MRI matches 4D-CT motion only in mid-respiratory phases

by Junchao Li, Shengqi Chen +4 more

The Role of Free-breathing GRASP MRI in Accurate Phase Matching with 4D-CT for Motion Representation in Liver Cancer Radiotherapy

Free-breathing scans accurately represent liver tumor motion at 30-60% phases but not at inhale or exhale extremes, requiring combined imagi

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Objective: To determine whether free-breathing golden-angle radial sparse parallel (GRASP) magnetic resonance imaging (MRI) can represent respiratory-induced organ motion in patients with liver malignancies undergoing stereotactic body radiation therapy (SBRT). Methods: A retrospective analysis of 54 patients undergoing liver SBRT was conducted. Four-dimensional computed tomography (4D-CT), the gold standard for motion assessment, was used to characterize liver tumor motion. Image fusion was performed between free-breathing GRASP MRI and each respiratory phase of 4D-CT using an in-house registration program, with fusion quality quantified by maximum cross-correlation coefficient (MCC). Validation involved two blinded radiation oncologists: one repeated image fusion using the Eclipse-built-in module, while the other evaluated clinical relevance on a five-point scale. Results: The 50% respiratory phase of 4D-CT achieved the highest fusion quality with GRASP MRI, showing no significant differences compared to the 30% (P = 0.106), 40% (P = 0.632), and 60% (P = 0.792) phases. In contrast, fusion quality declined significantly beyond the mid-respiratory window (30%-60%), with poor fusion at the 0%, 10%, 20%, 80%, and 90% phases (P < 0.001). Validation by radiation oncologists corroborated these findings, with the 50% phase achieving the highest score. Subjective scores remained above 4 for phases 30%-70%, while scores for the remaining phases fell below 4. Conclusion: Free-breathing GRASP MRI cannot independently represent organ motion across all respiratory phases; it accurately characterizes motion only within the mid-respiratory phases (30%-60%), with optimal performance at the 50% phase. When used as a delineation standard in liver SBRT, GRASP MRI should be combined with 4D-CT or dynamic imaging modalities to ensure comprehensive motion assessment and accurate target volume definition.

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physics.med-ph 2026-06-08

99 mouse brain ULM scans released as 30TB open dataset

by Brice Rauby, Nin Ghigo +14 more

ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging

The acquisitions include raw data, trajectories and metrics to support algorithm validation and machine learning across varied protocols.

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Ultrasound Localization Microscopy (ULM) enables microscopic imaging of the cerebral microvasculature in vivo, but relies on a multi-stage processing pipeline in which acquisition settings and reconstruction processes strongly influence the final output. Existing public datasets remain sparse, restricting rigorous evaluation and slowing progress in algorithm development, including emerging machine-learning approaches, which by design require large quantities of data to be robust and reliable. We introduce \textbf{ULMShare}, an open-access dataset of 99 whole-brain transcranial ULM acquisitions from 61 healthy mice (36 females, 22 males, 3 unknown; mean age: $8.2 \pm 5.5$ weeks; mean weight: $17.7 \pm 4.2$ g), for a total of 30TB of raw data. The dataset spans three experimental procedures, multiple injection and anesthesia protocols, two ultrasound probes, and different imaging planes and orientations. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction and the corresponding microbubble trajectories. Alongside the data, we report vascular saturation, Fourier Ring Correlation, and track-length statistics, plus expert visual gradings. ULMShare provides a broad, standardized and publicly available resource for method development, validation, and benchmarking. The full dataset is available on the Federated Research Data Repository and additional resources are hosted on the ULMShare Github repository.

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physics.flu-dyn 2026-06-08

Tear film rupture localizes in mucin-deficient patches

by Deepak Kumar, Pushpavanam S

Effect of Spatially Heterogeneous Mucin Coverage on Tear Film Stability and Ruptur

Disjoining pressure distribution sets breakup sites and produces rupture times matching clinical observations.

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Clinical observations of dry eyes reveal that tear film breakup is associated with spatial variations in corneal wettability arising from non-uniform mucin coverage. Motivated by these observations, we develop a thin-film model to investigate the influence of heterogeneous wettability on tear film stability. Heterogeneity in mucin coverage is incorporated through variations in the Hamaker constant and slip length along the corneal surface. Two representative forms of spatial heterogeneity are considered: a periodic step variation representing sharply localised mucin-deficient patches and a smoothly varying sinusoidal distribution representing gradual changes in glycocalyx. The steady states are obtained by a balance between capillary and van der Waals forces. A linear stability framework based on Floquet-Bloch theory and a discretised eigenvalue approach is developed to account for the periodic coefficients in the linearised equations. We show that heterogeneous wettability induces coupling between perturbation modes. The most unstable wavenumber and the maximum growth rate decrease with increasing mucin coverage fraction. However, both increase with increasing Hamaker constant contrast between mucin-rich and mucin-deficient regions. Nonlinear simulations reveal that rupture preferentially localises within mucin-deficient regions irrespective of the initial film thickness. The rupture location is governed by the spatial distribution of disjoining pressure rather than the initial perturbation. The predicted rupture dynamics are consistent with clinical observations where rupture location is invariant and the rupture times obtained from the model are in good agreement with clinically reported values. These findings demonstrate that spatial heterogeneity in wettability plays a decisive role in tear film instability and must be incorporated in tear film dynamics models.

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physics.med-ph 2026-06-08

Jet injectors load tissue with normal and shear stresses

by Kohei Yamagata, Prasad Sonar +3 more

Time-Resolved Stress Analysis of Tissue Simulants During Needle-Free Jet Injection

High-speed photoelastic imaging shows normal-stress differences become comparable to or exceed shear depending on injector and cavity shape.

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Needle-free jet injection generates transient internal stress fields that can influence tissue deformation, pain-related stimulation, and cellular-level mechanical responses. However, the penetration mechanics have often been inferred from cavity deformation and interpreted mainly as shear-dominated behavior. In this study, high-speed photoelastic measurements were used to visualize and quantify optically integrated stress responses in a 5 wt% gelatin tissue simulant during penetration by two needle-free injectors with different actuation mechanisms: the Actranza Lab, a pyro-drive injector driven by cartridge-based combustion, and the Biojector 2000, a commercially available CO$_2$-driven injector. A polarization camera operated at 60,000 fps was used to obtain the phase difference and principal stress orientation, allowing evaluation of the photoelastic stress-intensity response and its decomposed normal- and shear-stress-related components. Under the same injection volume of 20 $\mu$L, the Actranza Lab formed a narrow, depth-oriented cavity, whereas the Biojector 2000 produced a wider, bulged cavity. In both cases, a clear normal-stress-difference component developed around the cavity. This component became comparable to or greater than the shear-stress component for the Actranza Lab and became dominant during the later cavity-bulging stage for the Biojector 2000. These results show that needle-free jet penetration cannot be described solely by shear stress; instead, injector-dependent cavity dynamics generate multi-component tissue loading. The findings provide an engineering basis for evaluating needle-free injector performance and for designing systems that improve delivery while reducing mechanical burden on tissue.

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physics.med-ph 2026-06-08

First fetal aorta model shows coarctation sharply raises wall shear stress

by Kamlesh Kumar, Biao Si +3 more

The First Human-Based In Vitro Flow Loop and Quantification for Fetal Aortic Hemodynamics

Three-component PIV reveals up to 29 percent underestimation by standard 2D methods and points to new hemodynamic biomarkers.

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Coarctation of the aorta (CoA) is a common congenital defect that remains difficult to diagnose prenatally due to subtle and evolving anatomical features. In the fetus, the ductus arteriosus creates a dual-inflow configuration that generates complex three-dimensional flow patterns not captured by standard imaging. Improved characterization of fetal hemodynamics may enhance diagnostic accuracy beyond anatomy-based assessment. This study presents the first human-based in vitro flow loop of the fetal aorta, constructed from anatomies reconstructed using medical imaging data. Models representing normal and coarctation conditions were fabricated and integrated into a physiological flow loop. Velocity fields were measured using planar and stereoscopic particle image velocimetry (PIV) to resolve near-wall and three-dimensional flow structures, enabling quantitative assessment of velocity gradients and wall shear stress (WSS) under normal and coarctation configurations. The in vitro flow loop closely reproduced target fetal flow segmentation, with segmental flow-rate errors generally below 6%. High-resolution planar and stereoscopic PIV revealed dual jets from the ascending aorta and the ductus arteriosus and predominantly planar flow in the normal aorta, but strong jet acceleration, separation, and reattachment in the coarcted geometry. Coarctation produced markedly elevated and spatially heterogeneous WSS, and 2-component PIV underestimated WSS by up to ~29% compared with 3-component measurements, especially in high-shear regions. These findings show that accurate three-component velocity measurements are critical for reliable WSS estimation and suggest that detailed hemodynamic metrics, such as WSS, may serve as potential biomarkers to enhance fetal CoA diagnosis beyond anatomy alone.

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cs.AI 2026-06-05

Hybrid models merge physics equations with neural nets for brain disorders

by Shah Pallav Dhanendrakumar, Saikat Pal +1 more

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming

They promise better diagnosis, progression forecasts, and treatment for Alzheimer's, tumors, and stroke than either method alone.

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Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning. Mechanistic models provide valuable scientific insight into the disorders, but in practice they are often simplified with assumptions or computationally expensive and slow to solve. However, while purely data driven approaches provide speed and scalability, they require large, high quality data to train and generally suffer from interpretability and generalization issues. This perspective paper presents a structured overview of hybrid modeling strategies, which combine deep learning models with physics based solvers, and are categorized into parallel, series, and parallel-series architectures. Three main approaches that have been emphasized are residual modeling for missing or incomplete physics, Neural Ordinary Differential Equations (NODEs) for continuous time dynamics approximation, and solver in the loop that accelerates traditional solvers with neural approximations. These hybrid models integrate the governing differential equation based formulations and deep learning to characterize the evolution of neurological disorders, and promise advanced personalized neurological modeling. In addition, the study explores and proposes different hybrid configurations to improve diagnosis accuracy, predict disease progression, and inform treatment strategies across a range of neurological disorders. These capabilities outperform standalone mechanistic or purely data driven approaches, making hybrid modeling a powerful tool, especially in applications involving modeling the progression and treatment responses in neurological conditions such as brain tumors, Alzheimer's disease, and stroke.

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physics.med-ph 2026-06-05

Nonlinear acoustics replace MRI for transcranial lens alignment

by Pradosh Pritam Dash

Transcranial FUS Therapy and Monitoring using Nonlinear Acoustics

Topology optimization designs the holograms while the parametric array effect handles positioning and pressure tracking.

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Focused ultrasound (FUS) offers a promising, non-invasive method for modulating neural activity and delivering therapies deep within the brain with immense clinical potential. However, progress in developing transcranial ultrasound (TUS) for clinical applications has been hindered by several factors. The complexity of the human skull causes focal aberrations and attenuation, thereby presenting a major obstacle to the precise targeting of ultrasound waves. Although phased arrays can correct for these aberrations, their high cost and continuous reliance on magnetic resonance imaging (MRI) pose significant obstacles for widespread academic research and clinical translation. To address these challenges, this thesis proposes an innovative framework for the design, registration, and clinical application of acoustic holograms. First, we introduce a novel frequency-domain topology optimization method that overcomes the breakdown of traditional phase-only designs in the megahertz regime by accounting for volumetric wave-propagation effects, thereby achieving high-fidelity focusing. Second, we present a non-invasive registration strategy that utilizes the nonlinear parametric array (PA) effect to enable precise lens alignment without requiring any imaging modalities, such as MRI. Finally, we demonstrate the utility of this nonlinear parametric array (PA) effect as a tool for monitoring ventricular dilation as a non-invasive proxy for intracranial pressure changes in hydrocephalus. Collectively, these developments provide a path toward accessible, high-precision transcranial ultrasound systems for research and clinical use. In addition, we demonstrate a novel platform for in vitro focused ultrasound neuromodulation that leverages acoustics to advance therapeutic discovery.

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physics.med-ph 2026-06-04

Anisotropy alters auxetic skin graft mesh expansion

by Masoumeh Razaghi Pey Ghaleh, Denis O'Mahoney +1 more

Influence of anisotropy on the expansion performance of auxetic skin meshing geometries: a finite element study

Lowest performance when Langer's lines are transverse to load; isotropic models overpredict stress

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This study investigates the combined effects of anisotropy and auxetic mesh geometry on the performance of skin graft expansion. Finite element models of auxetic slit-based geometries were developed and subjected to 25 percent tensile strain. Skin was modelled using an anisotropic constitutive formulation. Langer's line orientations were varied relative to the load direction. Results showed anisotropy strongly influenced expansion behaviour. The effect was observed to be complex and highly dependent on mesh type. Anisotropy was observed to enhance or inhibit the auxetic expansion behaviour. In all mesh types studied, the expansion performance is lowest when Langer's lines align with the transverse direction. Greatest expansion was typically observed when Langer's lines were close to the loading direction. Isotropic models overpredicted stress relative to the anisotropic models. These findings support the use of auxetic structures for skin mesh expansion applications and show that anisotropy is an important factor in both deformation and stress prediction.

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eess.IV 2026-06-04

Prior scans speed MRI reconstruction without registration

by Arda Atal{i}k, Sumit Chopra +1 more

L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI

L-TGVN limits old data influence to match new measurements, yielding better detail at high accelerations while tolerating misalignment and p

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MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam costs and limiting scanner throughput. A common approach to reduce scan time is to acquire fewer measurements, which yields an ill-posed linear inverse problem; recovering diagnostic-quality images therefore requires incorporating prior knowledge beyond the measured data. In follow-up exams, the most recent prior scan of a patient can provide a highly informative subject-specific context, but practical use is complicated by temporal changes (including pathology progression), misalignment between scans, and protocol drift across acquisitions. In this work, we introduce L-TGVN, a Longitudinal Trust-Guided Variational Network that leverages prior scans as side information to reconstruct the current scan from heavily undersampled measurements. Crucially, L-TGVN constrains the influence of prior scans to be consistent with the acquired measurements. Unlike many existing longitudinal reconstruction methods, it does not require explicit pre-registration between prior and current scans. It further accommodates differences in acquisition protocols across visits (e.g., changes in sequence parameters). We evaluate L-TGVN against matched-capacity baselines, including prior-guided methods and methods that do not use longitudinal priors, and observe consistent improvements in standard quantitative metrics together with better preservation of fine structures at challenging accelerations. Source code is available at github.com/sodicksonlab/L-TGVN.

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physics.med-ph 2026-06-03

Nozzle-mounted cameras detect mm proton range shifts

by Farshad Safavi, Stephen W. Peterson +6 more

The First Nozzle-Mounted Compton Camera Prompt Gamma Imaging System for In Vivo Proton Therapy Dose Verification

Quad Compton camera system integrated on treatment nozzle tracks emission changes during clinical proton beam delivery.

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This study presents the first clinical integration and experimental demonstration of a nozzle-mounted Compton camera prompt gamma imaging (PGI) system for in vivo proton range verification. Four position-sensitive solid-state Compton camera modules, each containing four cadmium zinc telluride (CdZnTe) detector crystals, were integrated into a modified range shifter mounted directly on the treatment nozzle of a clinical proton therapy gantry. This compact fixed-geometry configuration maintained alignment with the proton beam axis throughout irradiation and enabled stable synchronized data acquisition during pencil-beam scanning delivery. The system was evaluated under realistic clinical proton beam delivery conditions using single-energy and spread-out Bragg peak (SOBP) irradiations at gantry angles of 90{\deg} and 270{\deg}, delivered doses of 2 Gy and 7.5 Gy, and controlled distal range shifts of up to 10 mm. Prompt gamma events were reconstructed into three-dimensional emission distributions using a physics-based Compton scatter reconstruction framework. The system operated reliably during all irradiations and produced reproducible prompt-gamma localization across repeated measurements. Reconstructed emission distributions remained geometrically consistent across gantry angles and demonstrated sensitivity to controlled distal range perturbations, with measurable upstream shifts of the emission hotspot corresponding to reduced proton penetration depth. These results demonstrate the feasibility of a clinically integrated nozzle-mounted quad-camera Compton PGI system for detecting millimeter-scale proton range variations during beam delivery and represent an important step toward clinically deployable prompt gamma-based in vivo treatment verification in proton therapy.

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physics.med-ph 2026-06-03

4D strategy enables magnet-free proton therapy for moving tumors

by Nair N von Muehlenen, Florentin Bieder +2 more

Magnet-Free Proton Therapy with 4D Pencil Beam Delivery Optimisation

Planning tool produces clinically acceptable doses using single-magnet setups without gantries, within practical times.

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Objective. Motion management is a critical challenge in proton therapy for mobile tumours. This study aims to develop and evaluate a novel four-dimensional (4D) pencil beam delivery strategy that incorporates respiratory motion into a dynamic treatment plan to improve dose conformity and treatment efficiency. Approach. To assess this 4D pencil beam delivery strategy, a mobile phantom was used. The generated 4D treatment plans were assessed with various scanner configurations, including gantry-free and magnet-free scanner heads. For each setup, the treatment time, dose conformity, and robustness against irregular breathing patterns were quantified. The influence of scanner head design and patient-specific motion irregularities on overall plan quality was evaluated. Main Results. The 4D planning tool generated treatment plans that achieved clinically acceptable dose distributions across all configurations. In magnet-free configurations, static beam operation substantially increased treatment time and reduced dose conformity. In contrast, configurations using a single scanner magnet, without a gantry, maintained acceptable conformity within practical treatment times. Significance. The proposed 4D delivery strategy demonstrates feasibility for treating mobile targets with simplified, gantry-free and magnet-free scanner designs. Further improvements could be achieved by synchronising the patient's breathing with 4D delivery, which may enhance dose accuracy during irregular or interrupted breathing. By reducing system complexity while preserving dosimetric performance, this approach offers a pathway toward more accessible and cost-effective proton beam therapy for motion-affected tumours.

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cs.LG 2026-06-02

Noise synthesis in training data removes bias from ML microstructure estimates

by Bradley G. Karat, Maëliss Jallais +4 more

Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning

Matching Rician expectation and effective variance to real signals brings supervised estimates in line with traditional fitting and improves

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Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepancies between the noise characteristics of simulated and acquired signals introduce a form of covariate shift, whereby the input signal distribution differs between training and inference. We investigated the impact of this mismatch on microstructure parameter estimation and propose a realistic noise synthesis (RNS) framework to mitigate it. RNS incorporates both the Rician expectation and the effective post-processing noise variance into simulated training signals. The Rician expectation was modelled using a noise standard deviation estimated with MPPCA, while the effective standard deviation was derived from spherical harmonic residuals of preprocessed data. The method was evaluated using the cylinder-zeppelin and the SANDI models on simulated datasets across multiple SNR levels and on in vivo diffusion data with repeated acquisitions. Sensitivity to noise misestimation was also assessed. Ignoring magnitude-induced noise effects during training produced systematic, SNR-dependent parameter bias, particularly at low SNR. Incorporating the Rician expectation substantially reduced bias to the level of noise-aware nonlinear least-squares fitting. Modelling the effective standard deviation further improved precision. Performance was largely independent of regression architecture but sensitive to accurate noise estimation. These findings demonstrate that realistic noise modelling in simulated training data mitigates signal-domain covariate shift and is essential for unbiased supervised microstructure estimation, particularly in low-SNR regimes associated with high b-values or high spatial resolution.

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physics.med-ph 2026-06-01

DRL knowledge guides LLM to optimal plans in fewer steps

by Md Mainul Abrar, Xun Jia +1 more

A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning

The hybrid agent reaches top scores across prostate and liver cases while cutting iteration count and generalizing without retraining.

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In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL) agent is transferred to an LLM agent through in-context learning, enabling autonomous iterative planning without human intervention. While standard LLM-based planning often lacks physical intuition and struggles with convergence, the integration of DRL-derived guidance constrains the agent to a physically valid parameter space. Experimental evaluations are performed across three diverse planning scenarios: basic prostate cases, complex prostate configurations with increased organ-at-risk (OAR) constraints, and liver cases. The evaluation results demonstrate that the guided LLM agent consistently achieves optimal planning scores while significantly reducing the number of iterations compared to unguided planning. Analysis of the final TPP configurations reveals that the agent successfully learns a hierarchical priority of objectives, effectively restoring a logical "cause-and-effect" relationship between parameter tuning and dosimetric outcomes. Crucially, this prototype framework exhibits robust generalizability, maintaining high planning quality regardless of specific patient anatomy, treatment site, or initial plan quality. By bridging the specialized optimization of DRL with the adaptive reasoning of LLMs, this M2M framework establishes a scalable foundation towards generalizable autonomous treatment planning, ultimately benefiting clinical practice in realistic environments.

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cs.CE 2026-05-29

Lumped RC circuit models human head for EQS brain studies

by Angelo Faccia, Ermanno Citraro +1 more

A Lumped-Element Electrical Model of the Human Head for Brain-Oriented Applications

Three concentric shells with radial and tangential branches match exact solutions and show dispersion matters for scalp potential accuracy.

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In this work, we present a compact surrogate circuit for electro-quasi-static (EQS) head modeling. A three-shell geometry (brain, skull, scalp) is considered, and each layer is modeled through radial and tangential pathways, implemented as RC branches. Frequency-dependent tissue conductivity and permittivity are mapped into dispersive resistive and capacitive elements. The model is validated against a semi-analytical spherical-harmonics reference solution over multiple geometrical configurations and operating frequencies, demonstrating good agreement. Neglecting dispersion and capacitive pathways can lead to an overestimation of scalp potentials over the considered frequency range, highlighting the need for dispersive RC circuit modeling.

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cs.CE 2026-05-29

Lumped RC circuit matches head tissue behavior to 50 kHz

by Angelo Faccia, Ermanno Citraro +1 more

A Lumped RC Equivalent Circuit of Head Tissues for Dispersive Neuro-Electromagnetic Modeling

Minimal equivalent circuit reproduces scalp voltages from brain dipoles across skull thicknesses and source positions.

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Accurate modeling of electric potential and current distribution in head tissues is crucial for the design and evaluation of neuro-sensing and neuro-stimulation systems operating in the sub-megahertz frequency range. Numerical methods are widely employed in electromagnetic simulations, however their computational cost can limit their applicability to rapid prototyping, real-time simulations, and circuit-level integration. In this work, we introduce a lumped RC equivalent circuit model that reproduces the electrical behavior of a canonical three-layer spherical head geometry over a frequency range up to 50 kHz. The model accounts for frequency-dependent tissue conductivity and permittivity to capture dispersive effects, employing complex conductivity in the electro-quasi-static (EQS) regime. The circuit topology uses a minimal set of impedance elements in order to represent the essential mechanisms of electric signal propagation. Validation was performed using a dipolar brain source configuration for scalp voltage peak estimation, showing close agreement with semi-analytical solutions across different skull thicknesses and dipole eccentricities. In addition, the impact of tissue dispersion and capacitive branches on the model predictions was quantitatively assessed, showing their contribution to the overall fidelity of the proposed approach.

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physics.med-ph 2026-05-29

Helium-ion CT keeps RSP error under 1 percent at 4 mGy dose

by Zsófia Jólesz, Gábor Bíró +2 more

CT Imaging with Helium and Carbon Ions for Hadron Therapy

Simulations show carbon ions lead in precision while protons minimize exposure, all avoiding X-ray conversion errors in hadron planning.

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Objective: To perform a comprehensive comparative analysis of proton, helium-ion, and carbon-ion computed tomography (CT) as direct imaging modalities for hadron therapy treatment planning, focusing on Relative Stopping Power (RSP) reconstruction accuracy and patient radiation dose. Approach: High-fidelity Monte Carlo simulations were conducted using the GATE/Geant4 platform to model a standard CTP404 phantom. RSP maps were reconstructed using an iterative Richardson-Lucy deconvolution algorithm. Imaging performance was evaluated by comparing reconstructed RSP values against ground truth data for various tissue-equivalent inserts, while integral doses were estimated for a human head geometry. Main results: All investigated particle modalities demonstrated a significant dose reduction compared to conventional X-ray CT protocols (which are approximately 40 mGy). The estimated imaging doses were 1.6 mGy for protons, 3.9 mGy for helium ions, and 22.7 mGy for carbon ions. In terms of accuracy, carbon-ion imaging achieved the highest fidelity for soft-tissue materials (mean absolute error <0.5\%). Helium ions offered a balanced performance with sub-1\% errors for most materials and a dose burden significantly lower than carbon ions. Protons exhibited the widest range of RSP deviations (-1.8\% to +3.1\%). Significance: Direct particle imaging eliminates the systematic uncertainties inherent in photon-to-hadron conversion. While carbon ions provide superior RSP reconstruction precision essential for complex treatment plans in heterogeneous anatomy, helium and proton imaging offer exceptional dose sparing, making them particularly advantageous for pediatric patients and frequent adaptive replanning scenarios.

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physics.med-ph 2026-05-29

Grating interferometry estimates spectra to correct dark-field artifacts

by Longchao Men, Peiyuan Guo +6 more

Coupling Spectrum Estimation and Single Energy Material Decomposition via X-ray Grating Interferometry

Step-wedge measurements recover energy and coupling spectra via EM, enabling artifact removal and single-energy material separation.

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Dark-field imaging based on grating interferometry is an emerging X-ray modality in medical imaging, which is particularly capable of providing complementary diagnostic information by visualizing the microstructural properties of lung tissue. However, quantitative dark-field imaging remains fundamentally challenged by beam hardening, which arises from the energy-dependent fringe visibility under polychromatic illumination. The resulting artifacts substantially degrade the quantitative accuracy of dark-field images. In this work, motivated by our key observation of an intrinsic similarity between the X-ray energy spectrum and the system-related coupling spectrum, we propose a unified framework to simultaneously and independently estimate both spectra. By measuring the transmission associated with the zeroth- and first-order components of the phase-stepping curve using solid step-wedge phantoms, the two spectra are robustly estimated via an expectation-maximization algorithm. The recovered spectra are subsequently incorporated into a physics-based correction model to mitigate beam-hardening-induced artifacts in dark-field imaging effectively. Furthermore, leveraging the inherent availability of two independent spectra within X-ray grating interferometry, we introduce a single-energy material decomposition method that achieves basis material imaging without dual-energy scans. Wave-optical simulations and experiments demonstrate accurate spectrum estimation, effective dark-field signal correction, and reliable material decomposition. Consequently, the proposed framework extends the diagnostic potential of X-ray grating interferometry beyond pulmonary imaging, facilitating broader applications in medical imaging.

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physics.med-ph 2026-05-29

ADMM splits fast and slow timing terms for better multi-kernel TOF-PET

by Kibo Ote, Fumio Hashimoto +4 more

Multi-Kernel TOF-PET Image Reconstruction Using ADMM

Separate optimization of CTR components under a constraint stabilizes convergence and raises contrast-noise ratio at early stopping points.

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Time-of-flight positron emission tomography (TOF-PET) detectors exhibiting multiple coincidence time resolution (CTR) components, such as those induced by the mixing of Cherenkov and scintillation photons, have attracted increasing attention. However, to fully exploit the latent potential of multi-kernel TOF-PET, new iterative image reconstruction methods are required. In this study, assuming that the events are labeled with the appropriate kernels, we propose an alternating direction method of multipliers (ADMM) for multi-kernel TOF-PET reconstruction, termed TOF-decomp ADMM. As the convergence speed of the TOF-PET log-likelihood depends on the CTR, the proposed method splits the fast- and slow-CTR log-likelihood terms and optimizes them separately under a constraint. This strategy explicitly balances the contributions of fast- and slow-CTR components and enables early stopping at iterations that yield improved contrast-noise trade-offs compared with conventional methods. We validated the proposed method using brain and image quality phantom simulations, demonstrating improved contrast-noise characteristics from a more stabilized convergence. By addressing the convergence imbalance inherent to multi-kernel TOF-PET, this work establishes a framework for exploiting the timing information available in emerging detector technologies.

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math.OC 2026-05-28

Background estimation cuts non-uniformity in wide-angle DBT

by Emil Y. Sidky, Adrian A. Sanchez +5 more

Improving depth-resolution, in-plane contrast, and reducing non-uniformity artifacts for wide-angle DBT

Two-stage sparsity method absorbs low-frequency artifacts while preserving quantitative accuracy on patient data.

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Purpose: This work aims to develop an image reconstruction algorithm for wide-angle digital breast tomosynthesis (DBT) that has improved depth resolution and in-plane contrast while reducing non-uniformity artifacts. Approach: The image reconstruction algorithm is an extension of our prior work on sparsity-regularized iterative image reconstruction. The algorithm is performed in two stages as explained in a prior work. The first stage consists of a low-resolution reconstruction that exploits sparsity for quantitative accuracy. In this work, this first stage is augmented with a formulation that includes the estimation of a "background" image, which absorbs low-frequency artifacts that cause image non-uniformity. Results: The new algorithm is demonstrated on a patient case for which the data are acquired on a wide-angle DBT system. Conclusion: The results on the shown case indicate that the algorithm design goals have been met, but additional empirical results and task-based assessment are needed to strengthen this conclusion.

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cs.SD 2026-05-28

Chest accelerometer matches mic on infant cry F0 and jitter

by Winko W. An, Saketh Sundar +3 more

Cross-modal characterization of infant cry: validation of a chest-surface accelerometer in extracting acoustic vocal function measures

Device captures key vocal measures from 85 babies while resisting noise and avoiding privacy issues of microphone recordings.

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Background: Infant cry acoustics provide a promising window into early neurodevelopment and may serve as scalable biomarkers for neurodevelopmental disorders. However, conventional microphone-based recordings are highly susceptible to environmental noise and raise privacy concerns in real-world clinical settings. Chest-surface accelerometers may offer a robust alternative by capturing vibrations directly from the larynx. Methods: We evaluated the validity of a chest-mounted accelerometer (ACC) for infant cry analysis by comparing acoustic features derived from ACC and simultaneously recorded microphone (MIC) signals during routine vaccination visits. The final sample included 85 infants (41 at 4 months; 44 at 12 months) from a diverse pediatric population. Seven vocal measures were extracted from both modalities, including fundamental frequency (F0), jitter, shimmer, cepstral peak prominence (CPP), and harmonics-to-noise ratio (HNR). Agreement and consistency between modalities was assessed using intraclass correlation coefficients (ICCs). Results: F0 demonstrated excellent agreement between ACC and MIC recordings (ICC > 0.94). Jitter measures also showed good-to-excellent agreement, while CPP demonstrated moderate agreement. Shimmer and HNR showed lower absolute agreement and systematic bias between modalities, reflecting possible differences in signal transmission and noise sensitivity. Conclusion: In summary, chest-surface accelerometers can reliably capture several clinically relevant acoustic features of infant cry, particularly temporal measures of F0 and jitter. This approach offers a noise-robust and privacy-preserving alternative to microphone-based recordings, supporting its potential use in scalable clinical and developmental research applications.

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cs.CV 2026-05-28

Segmentation priors turn 64 mT MRI into 3 T quality

by James Grover, Andrew Phair +2 more

Enhancing Ultra-low-field MRI with Segmentation-guided Adversarial Learning

CycleGAN and T-REX networks guided by ULF-trained tissue maps match high-field scans in both metrics and appearance.

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Ultra-low-field (ULF) MRI offers portable and low-cost imaging but suffers from poor image quality. To address this, we present our submission to the 2025 ULF Enhancement Challenge (ULF-EnC), where the goal is to synthesise high-field-like MRIs from 64 mT scans. Our pipeline enhances ULF MRI through a combination of anatomical conditioning and model ensembling. We first generate tissue segmentation priors using a Swin UNETR trained solely on challenge-provided data. These priors condition two independent enhancement networks - a CycleGAN and a transformer-based residual enhancement model (T-REX) - each trained to synthesise 3 T-like MRIs. Outputs from both models are combined using a weighted average. Our approach produces enhanced MRIs that were comparable to high-field scans both quantitatively and qualitatively.

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