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Saturday 12 September

Medical Physics

Dosimetric equivalence of deep learning prostate contours after LDR brachytherapy: pre-declared margins, patient-level acceptance thresholds and the incremental predictive value of DVH indices

Louis-Bernard St-Cyr, Anne Saint-Laurent, José Angel Lesteiro-Tejeda et al. (+7 more)

Background and purpose: Dose Volume Histogram (DVH) indices remain the main dose-effect metrics for toxicity prediction, but they depend on how contours were made. Inter-observer variability (IOV) is unavoidable and clinically accepted, so the question for automatic segmentation addresses equivalence: do automatic contours produce DVH errors comparable to human IOV, and can these indices predict patient-reported toxicity? Materials and methods: In 429 patients treated with iodine-125 LDR-BT monotherapy, indices from expert manual delineation were compared with a deterministic and a Bayesian nnUNet on a fixed dose distribution, by two-one-sided tests against margins set from CT contouring IOV. Logistic regression gave patient-level thresholds at 90\,% probability of equivalence. In 380 patients, eleven DHV indices were added to a clinical baseline predicting change in International Prostate Symptom Score (IPSS) at six horizons over 5 years, with nested cross-validation, bootstrap intervals and corrected $t$-tests. Results: All cohort-level comparisons of DVH indices were declared equivalent, with no interval consuming more than 44\,% of its equivalence margin. Individual agreement was weaker with equivalence rates from 55.9\,% to 89.7\,%, depending on the DVH index and automatic segmentation model. Thresholds ranged from 0.864 to 0.958 Dice. No DVH block improved IPSS prediction at any horizon. The largest improvement declared by bootstrap intervals was 0.12 IPSS points, well below the minimal clinically important difference, across all learners. Conclusion: Automatic contours matched expert dosimetry within human IOV on average, but individual equivalence requires a volume dependent quality metric threshold definition. Our DVH indices panel provides no significant predictive power for IPSS, irrespective of segmentation source and horizon.
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All authors Louis-Bernard St-Cyr, Anne Saint-Laurent, José Angel Lesteiro-Tejeda, Sylviane Aubin, Marie-Claude Lavallée, Luc Beaulieu, Éric Vigneault, André-Guy Martin, François-Olivier Fabi, Louis Archambault

Medical Physics

Fail-closed conformal multiresolution error control for conservative three-dimensional dose remapping in synthetic phantoms

Yuntao Wang

Conservative dose remapping can be formulated by transporting mass and deposited energy across nonmatching grids, but fixed sampling uses the same number of points per active source cell regardless of case difficulty. We developed conformal multiresolution error control (CoMERC), a fail-closed multilevel quasi-Monte Carlo (QMC) controller for reference-relative target-cell allocation error in a fixed piecewise-constant source model with known deformation. Two randomized nested replicates were evaluated at a probe level of 4 and candidate levels of 8 and 16. Four endpoints were controlled jointly: reference-mass-weighted global, high-gradient, and density-gradient-threshold root-mean-square error, plus eligible-cell maximum absolute error. A split-conformal multiplier was calibrated from 240 synthetic cases, frozen, and evaluated on 400 independent cases from the same generator and 120 cases from a prespecified shifted generator. In the primary set, joint coverage was 394/400 (98.50%; one-sided 95% lower limit, 97.06%), 388/400 cases (97.00%; lower limit, 95.18%) received an output, and 0/388 released outputs exceeded any tolerance (one-sided 95% upper limit, 0.769%). Final actions were level 8 for 190 cases, level 16 for 198, and ABSTAIN for 12. The mean of the case-normalized, policy-implied production-sample ratios was 0.5844, with each case normalized to always running both randomized replicates through level 16; the prespecified 95th-percentile bootstrap upper limit was 0.6194. All seven primary gates passed. In the shifted-generator set, 113/120 cases were released and one released output exceeded a tolerance. CoMERC provided marginal finite-sample error control and lower modeled production-sample work in the locked synthetic population, but not patient-level, registration-level, conditional-on-release, or clinical safety validation.
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Friday 11 September

Medical Physics

The Electrodynamic Basis of Dichroism-Mediated Polarization Perception

Gary P. Misson, Dusan Sarenac, Dmitry. A. Pushin et al. (+1 more)

Humans see the polarization of light through entoptic percepts arising from the macula's Henle fiber layer, where xanthophyll pigments absorb preferentially across the radiating fibers. From the layer's complex dielectric tensor alone, Maxwell's equations in Berreman $4\times4$ form yield its Mueller matrix, set by four scalars $\{A,B,C,D\}$. Its intensity channel depends only on the dichroic pair $A$ and $B$, which fix the percepts at a maximum contrast $|B|/A\approx0.05$. One relation generates the whole dichroism-mediated family: Haidinger's brushes under a uniform field, their dark arms perpendicular to the $\mathbf{E}$-vector; fractured brushes under spatially varying fields; and $N$-fold brushes under vector-vortex illumination.
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All authors Gary P. Misson, Dusan Sarenac, Dmitry. A. Pushin, Shelby E. Temple

Medical Physics · Cross-listed from cs.CV

SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation

Lila Cunge

Single Ventricle Physiology (SVP) is a rare subtype of congenital heart disease characterized by the presence of a single functional cardiac ventricle with atypical anatomic configurations that challenge conventional image segmentation approaches. The scarcity of clinical data and the morphological diversity across SVP subtypes make the development of robust segmentation methods particularly difficult. To address these limitations, we propose a cardiac MRI segmentation framework focused on ventricular chambers and myocardium segmentation tailored for SVP. First, we introduce a data augmentation pipeline that generates synthetic 3D cardiac meshes using SDF4CHD and corresponding synthetic cardiac MRI through generative modeling. Second, we introduce SV-Cine, a diagnosis-conditioned adaptation of the foundation model CineMA that incorporates patient-level diagnostic information through Feature-wise Linear Modulation layers, enabling diagnosis-aware feature adaptation during segmentation. We evaluated the framework on an internal cohort with varying SVP subtypes. SV-Cine achieved median Dice scores of 0.89 (IQR: 0.80–0.91) for the left ventricle and 0.72 (IQR: 0.54–0.84) for the right ventricle, outperforming the strongest baseline, nnU-Net, by 0.39 Dice points on right ventricle segmentation. It also yields a median ejection fraction error of 5.55 percentage points (IQR: 3.41–7.69) for the dominant ventricle. Compared with the internal cohort, LV and myocardium segmentation performance was lower for the external cohort; whereas RV Dice scores were comparable for both cohorts. Our findings suggest that a pretrained foundation model can be adapted for highly specialized downstream tasks through usage of diagnosis priors while leveraging anatomic knowledge learned from large-scale MRI datasets during pretraining.
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Thursday 10 September

Medical Physics

Phase-contrast micro-CT for intra-operative breast tumour margin assessment using a microfocus x-ray source and photon-counting detector

Michelle K. Croughan, Timur E. Gureyev, Jane Fox et al. (+5 more)

Objective: Intra-operative tumour margin assessment during breast-conserving surgery requires rapid, high-resolution imaging of excised tissue, allowing the surgical team to take appropriate action within a single operation. This study evaluates a custom propagation-based phase-contrast micro-computed tomography (micro-CT) system designed to meet these clinical constraints without specialised optical elements. Methods: The experimental setup pairs a microfocus x-ray source with a photon-counting detector in a cone-beam geometry. We explore how the spatial coherence of the source can provide propagation-based phase contrast – with no additional specialised optical elements – and balance this against maximising the x-ray flux of the cone-beam geometry. System performance was evaluated across two anode target materials and filtration configurations at various tube power settings. Imaging capabilities were validated using anthropomorphic breast tissue phantoms and a formalin-fixed paraffin-embedded (FFPE) breast tissue specimen, with reconstructions compared against gold-standard histology. Results: An unfiltered tungsten target operated at 40 kVp yielded optimal image quality. The optimised system achieved high-resolution CT reconstructions of a 5 cm diameter sample with an isotropic voxel size of 40.7 $\upmu\text{m}$ in a scan time of 12 minutes. Reconstructed volumes demonstrated strong visual correlation with corresponding histology slides. Conclusion: Combining a microfocus source with a photon-counting detector enables high-resolution, phase-contrast micro-CT within a clinically viable timeframe, demonstrating strong potential for intra-operative margin assessment.
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All authors Michelle K. Croughan, Timur E. Gureyev, Jane Fox, Mikkaela McCormack, James A. Pollock, Dominic Jurkschat, Stephanie A. Harker, Marcus J. Kitchen

Wednesday 9 September

Medical Physics · Cross-listed from q-bio.QM

PyFLI: A Python Library for Simulation, Parameter Estimation, and Benchmarking in Fluorescence Lifetime Imaging

Vikas Pandey, Ismail Erbas, Margarida Barroso et al. (+2 more)

Fluorescence lifetime imaging (FLI) measures the temporal decay of fluorescence after excitation and provides quantitative information about a fluorophore's local environment and molecular interactions. Depending on the fluorophore and experimental design, lifetime can report changes associated with pH, oxygenation, cellular metabolism, and Forster resonance energy transfer. These properties make FLI useful across microscopy, biophysics, biomedical optics, and preclinical imaging, where the same type of molecular contrast can be studied across different biological scales. FLI measurements, however, are acquired with instruments that record fluorescence in different ways. Intensified charge-coupled device (ICCD) cameras, single-photon avalanche diode (SPAD) arrays, and time-correlated single-photon counting (TCSPC) systems differ in temporal sampling, data organization, detector noise, instrument response, and file format. Lifetime-estimation methods also make different assumptions about the recorded decay, while learning-based approaches require realistic training and validation data for which the underlying parameters are known. PyFLI is an open-source framework for FLI processing and standardized data simulation. It imports measurements from acquisition systems, simulates labeled data under configurable acquisition and noise conditions, and provides complementary approaches for parameter estimation. These include nonlinear least-squares fitting (NLSF), maximum-likelihood estimation (MLE), phasor analysis, rapid lifetime determination (RLD), Laguerre-based estimation, and optional Bayesian and deep-learning inference. CPU and GPU processing support image-scale analysis, while reconstruction, visualization, statistical analysis, and cross-software comparison provide tools for evaluating results. PyFLI includes compressed-sensing reconstruction for single-pixel hyperspectral FLI.
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All authors Vikas Pandey, Ismail Erbas, Margarida Barroso, Stefan Radev, Xavier Intes

Medical Physics

Understanding Search and Decision Errors in Liver Metastasis Detection and the Effects of Lower Radiation Dose

Parmita Mondal, Rickey Carter, Andrew D Missert et al. (+7 more)

The detection performance of liver metastases decreases with the reduction of radiation dose, but misses are heterogeneous. Previous eye tracking work has characterized missed metastases into two categories: search errors i.e., the eyes never land on the lesion, and decision errors i.e., the lesion is seen but not recognized as malignant. We integrated three prior reader studies to answer this question. In all studies, radiologists interpreted the same set of 40 contrast enhanced abdominal CT exams containing 91 liver metastases whose locations had been previously marked. In two studies, the workstation recorded their gaze and eye movements. Using eye dwell times, metastases were classified as search-error-dominant (majority of misses had <2 sec gaze time) or decision-error-dominant (>2 sec gaze time). In the third study, exams were interpreted both at 120 and 200 quality reference mAs (QRM) by ten radiologists. The third study did not include eye tracking. Out of 91 liver metastases, we excluded 16 that were never missed in the eye tracking studies and used 75 liver metastases for the present study.
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All authors Parmita Mondal, Rickey Carter, Andrew D Missert, Akitoshi Inoue, Joel G. Fletcher, Hao Gong, Shuai Leng, Lifeng Yu, Cynthia H. McCollough, Scott S. Hsieh

Tuesday 8 September

Medical Physics

Develop and Optimize 5DCT Imaging Simulation and Reconstruction Methods

Yuhao Wang, Zhendong Zhang, Edward Robert Criscuolo et al. (+4 more)

Purpose: To develop and optimize a 5DCT (3D + cardiac phase + respiratory phase) imaging simulation and reconstruction pipeline, and to compare two sinogram-space interpolation methods for reconstructing images at arbitrary combinations of cardiac and respiratory phase. Methods: Helical CT projections were simulated from the 4D XCAT phantom across a range of cardiac and respiratory motion states, with Poisson and electronic noise added. Ground-truth-matched volumes were generated at 5 cardiac phases and 10 respiratory amplitudes (50 total phase combinations). Because acquired projections are sparsely and unevenly distributed across this joint phase space, each target slice was reconstructed by interpolating rebinned sinogram rows to the target cardiac phase and respiratory amplitude, using either 2D scattered barycentric interpolation or 2D scattered local linear interpolation with a circular kernel for cardiac phase. Reconstructed volumes were compared to phantom ground truth using mean absolute error (MAE), and to conventional respiratory-gated 4DCT (r4DCT) reconstructed from the same simulated data. Results: Both interpolation methods eliminated the severe axial misalignment artifacts present when helical projections were reconstructed without phase-space interpolation. Local linear interpolation achieved lower MAE than barycentric interpolation across most tested conditions, with the largest improvement at low pitch. The 5DCT pipeline also produced respiratory-only volumes with fewer residual cardiac-motion artifacts than conventional r4DCT reconstructed from the same projection data, including at standard clinical pitch (0.1). Conclusions: 5DCT reconstruction using sinogram-space interpolation is feasible and can jointly resolve cardiac and respiratory motion with better accuracy than conventional 4DCT reconstruction.
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All authors Yuhao Wang, Zhendong Zhang, Edward Robert Criscuolo, John Ginn, Ke Lu, Yao Hao, Deshan Yang

Medical Physics · Cross-listed from physics.optics

Evaluating the predictive power of pump-probe imaging contrast of melanin for metastatic outcome: A 71-patient study

David Grass, Emma Bao, Martin C. Fischer et al. (+3 more)

Significance: Most melanoma deaths arise from metastatic spread, yet current staging imperfectly identifies which primary tumors will progress. Melanin structure is altered during malignant transformation, and pump-probe microscopy (PPM) measures melanin excited-state dynamics in standard biopsy sections, offering molecular contrast that is complementary to morphology-based assessment. Aim: To determine whether PPM-derived melanin excited-state dynamics in primary cutaneous melanoma can predict metastatic outcome. Approach: We imaged unstained sections from 71 primary cutaneous melanomas and 18 melanoma metastases. Transient absorption curves were fit to a biexponential excited-state model, and the fit parameters were used as features for learning classifiers to predict metastatic outcome. Results: Melanin dynamics separated primary from metastatic tissue: both excited-state lifetimes were shorter in primaries, with common-language effect sizes up to 0.26. The same parameters did not separate primary tumors by outcome (effect sizes 0.41-0.61), and univariate logistic regression found no significant association for either the excited-state lifetimes or the ESA/GSB ratio. The best classifier reached 74% patient-level accuracy (AUC 0.73), a plateau reached by several unrelated architectures. Conclusions: PPM robustly distinguishes primary from metastatic melanoma tissue, consistent with progressive melanin disaggregation. While it is clear that pump-probe features of melanin thus correlate with tissue differences, melanin dynamics in single sections of the primary tumor, confounded by heterogeneity, do not give robust predictions of metastatic outcome.
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All authors David Grass, Emma Bao, Martin C. Fischer, M. Angelica Selim, Georgia M. Beasley, Warren S. Warren

Monday 7 September

Medical Physics

Reducing Hydrocephalus Shunt Revision Rates: A Computational Fluid Dynamics Study on Catheter Hole Design

Omar Said, Mingzi Li and Hanyu Gan

Proximal shunt obstruction is the leading cause of ventriculoperitoneal (VP) shunt failure in pediatric hydrocephalus and is closely tied to the near-wall shear environment at drainage holes. We used computational fluid dynamics (COMSOL) to decouple local wall-shear-stress (WSS) control via catheter-tip geometry from global drainage control via a valve opening. A cylindrical ventricle domain with Newtonian CSF ($\rho$ = 1000 kg/m$^3$, $\mu$ = 1 mPa$\cdot$s) and $\Delta P \approx$ 10 mmHg was solved under laminar Navier-Stokes flow while sweeping distal hole spacing, hole count/diameter, and a simplified valve constriction. Mesh-independence was achieved (minimum element size $\approx$ 0.021 mm; distal-hole velocity converged to $\approx$ 1.18$\times10^{-3}$ m/s). Perpendicular separation of the two most distal holes increased total outflow by $\approx$ 22% (2 mm vs 0.5 mm) and raised distal lateral-wall WSS by $\approx$ 19%, whereas longitudinal shifts of upstream holes produced only small changes. A valve opening near 0.166-0.17 mm set system drainage to $\approx$ 20 mL/h across geometries, indicating capacity is valve-limited while local WSS is geometry-controlled. The selected tip uses two conical holes (0.5 mm ID) in four rows with 1.4 mm spacing, concentrating WSS at the distal lateral walls, the surfaces where obstruction occurs, without exceeding clinical drainage targets. These findings yield design rules: prioritize distal-pair spacing, reduce hole count with smaller conical openings to elevate protective WSS, and regulate outflow with the valve. The framework provides a clear path to benchtop validation, long-term adhesion assays under mapped WSS, and anatomically realistic CFD.
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Saturday 5 September

Medical Physics

Distinct Radiobiological Responses to BNCT in SAS Oral Squamous Cell Carcinoma and MCF-7 Breast Cancer Cells

Yuxiang Zhao, Zhao Sun, Changming Wang et al. (+4 more)

This work compared the radiobiological responses of SAS oral squamous cell carcinoma cells and MCF-7 breast cancer cells following accelerator-based boron neutron capture therapy (BNCT). Neutrons were generated by bombarding a lithium target with proton beams, followed by moderation to obtain sufficient thermal neutrons for BNCT irradiation. Boronophenylalanine (BPA) was used as the boron delivery agent. BNCT-induced biological responses were evaluated by gamma-H2AX immunofluorescence staining, cell-cycle analysis, apoptosis analysis, and clonogenic survival assays. BNCT induced marked gamma-H2AX foci formation in both cell lines, indicating DNA damage-associated responses after irradiation. The two cell lines further showed distinct post-irradiation outcomes. SAS cells exhibited stronger clonogenic suppression and prominent G2/M accumulation, whereas MCF-7 cells showed sustained G0/G1 accumulation and delayed apoptosis. These results suggest that BNCT sensitivity is determined by both boron accumulation and cell-line-specific biological characteristics. This work provides experimental evidence highlighting the importance of tumor-dependent cellular responses in understanding and optimizing BNCT efficacy.
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All authors Yuxiang Zhao, Zhao Sun, Changming Wang, Jianghao Lai, Jie Zhou, Zhencen He, Zhimin Hu

Friday 4 September

Medical Physics

An interpretable model of spectral scattering of arterial pulse waves in the circle of Willis encodes occlusion location

Xun Huang

Background and objective. Carotid Doppler ultrasound is the most widely available bedside probe of cerebral haemodynamics, and machine-learning classifiers fed carotid velocity spectra can localise intracranial occlusions—but at the price of black-box models, thousands of training samples, and fragility to anatomical variants. We ask whether the same information can be obtained from an interpretable physical model. Methods. We formulate a linear frequency-domain one-dimensional model of a 26-segment circle-of-Willis network in which each cardiac harmonic propagates on a Womersley transmission line and is scattered independently by a lesion. The model is validated against a nonlinear one-dimensional solver and three-dimensional stenosis computations at two severities, coupled to a physics-based synthetic spectral-Doppler pipeline, and used to generate a 9{,}600-case virtual cohort for occlusion-localisation classification with physics-guided features. Results. Mean flow divisions agree with the nonlinear solver to three significant figures, harmonic magnitudes within a ratio of 0.90–1.04, and carotid waveforms to 6.1\,% in relative $L_2$ norm after a single kinematic area correction. The benchmark reproduces healthy flow splits within 1–9\,%, collateral-channel flows within 11–16\,%, and the collateral-pathway hierarchy. Physics-guided harmonic-ratio features localise twelve lesion classes at 95.1\,% accuracy from five training samples per class—over an order of magnitude fewer than waveform-driven convoluted neural networks; augmentation partially restores robustness to measurement noise and anatomical variants. Conclusions. The framework explains the physical origin of the empirically discriminative 2–12\,Hz Doppler band and makes occlusion localisation interpretable and sample-efficient.
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Medical Physics

A unified dielectric-tensor model of macular form birefringence and dichroism

Gary P. Misson, Dusan Sarenac, Dmitry. A. Pushin et al. (+1 more)

Optical anisotropy of the Henle fiber layer (HFL) underlies two macular polarization phenomena, form birefringence (the macular cross) and dichroism (Haidinger's brushes), which have conventionally been treated as unrelated. We model the HFL as a radial diattenuating retarder and derive a single complex dielectric tensor: effective-medium theory for parallel cylinders gives the real part, and cylindrical averaging of the measured transmembrane xanthophyll tilt gives the imaginary part. The birefringent slow axis is radial and the absorption axis tangential, perpendicular by structural necessity. The model reproduces measured dichroic ratios of 1.04–1.14 for a single calibrated oriented-pigment fraction, and supplies the input parameters for full electromagnetic treatments.
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All authors Gary P. Misson, Dusan Sarenac, Dmitry. A. Pushin, Shelby E. Temple

Medical Physics

Attention-guided super-resolution of 4D flow MRI in carotid arteries

Ali Mokhtari and Dominik Obrist

Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizing and quantifying complex blood flow patterns in vivo. Despite its clinical promise, broader adoption is limited by low spatial resolution and sensitivity to noise, which restrict accurate assessment of critical hemodynamic biomarkers such as wall shear stress, pressure gradients, and turbulent kinetic energy. To overcome these challenges, we propose a deep learning-based super-resolution framework that integrates multi-scale feature extraction and attention mechanisms to enhance the quality of 4D flow MRI data. The model was trained on a dataset of 120 patients with 240 stenosed carotid arteries. High-resolution ground truth data were generated using patient-specific computational fluid dynamics (CFD) simulations based on segmented vascular geometries and physiologically realistic boundary conditions, and the resulting velocity fields served as targets for supervised learning. The proposed architecture uses convolutional block attention modules (CBAM) to guide the network toward clinically relevant spatial features and to suppress noise in low-resolution inputs. Quantitative results show that the attention-guided model substantially reduces the root mean square error (RMSE) compared with a baseline model without attention, and qualitative velocity contour analysis confirms improved reconstruction of intricate flow patterns. These findings highlight the capacity of the model to restore high-fidelity flow fields under noisy conditions and support the use of deep learning to extend the clinical utility of 4D flow MRI for non-invasive hemodynamic assessment.
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Medical Physics

BEAM3R: Beam's-eye-view architecture with Mamba-3 for implicit dose reconstruction

Chen Cheng, Michael Ferraro, James Grover et al. (+2 more)

To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimation framework operating in beam's-eye-view (BEV). Our core innovation combines a Mamba-3 state-space depth-sequence core with physics-based transport conditioning to model long-range depth transport without expensive 3D convolutions. BEAM3R shares a 2D CNN encoder-decoder architecture for photon and proton dose tasks, processing per-plane BEV slices. Proton beamlets are conditioned on water equivalent thickness and remaining range, encoding the parameters determining Bragg peak position. Photon models use a bidirectional Mamba-3 core to capture dose contributions from materials downstream of the calculation point, while the proton model uses a forward core with learned energy-prefix tokens and a Bragg-peak refinement module. To reduce interpolation artifacts and support high spatial resolution, we introduce axial grid alignment of BEV lattices with CT slices and an implicit super-resolution representation via sub-pixel phase packing, evaluated by a differentiable Triton-accelerated resampler that reconstructs packed cubic B-spline coefficients directly in CT space. For MRI-based tasks, synthetic CTs (sCT) are generated by a patch-based conditional GAN with a SwinUNETR backbone. On the preliminary DoseRAD2026 test set, CT-to-photon and CT-to-proton models achieved 1%/1 mm local gamma pass rates of 96.8% and 96.0%, with stratified plan-level MAEs of 0.0041 and 0.0079. Substituting sCT reduced gamma pass rates to 89.7% for photon and 75.4% proton plan level doses, with stratified plan-level MAEs of 0.0093 and 0.0336. Standardised runtimes were 23.4 s and 18.4 s for CT-to-photon and CT-to-proton prediction, increasing to 39.7 s and 42.8 s for the corresponding MRI-based pipelines.
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All authors Chen Cheng, Michael Ferraro, James Grover, David E J Waddington, Emily Hewson

Thursday 3 September

Medical Physics · Cross-listed from physics.optics

3D scattered light imaging: extracting 3D fiber orientations from 1D line profiles in brain imaging

Dennis Scheidt, Charlotte Voß, Cristian Rosero Arias et al. (+6 more)

Understanding the 3D fiber architecture of the brain at the microscopic scale is essential for revealing its structural connectivity and function. Polarization-based optical imaging (3D-PLI) techniques enable high-fidelity reconstruction of single nerve fiber orientations but struggle to resolve fiber crossings, which are critical for recovering the full connectome. Scattering-based imaging provides access to the structure factor of three-dimensionally oriented fibers. By probing a fixed scattering angle under multiple azimuthal illumination angles, in-plane fiber orientations and crossings can be recovered using computational scattered light imaging (SLI). However, despite containing 3D information, a theoretical framework to extract full 3D orientations has been lacking. In this talk, a simple analogical approximation of Rayleigh-Gans scattering theory is introduced to extract the 3D orientation of nerve fibers from one-dimensional SLI measurements. The theory is validated using tilted microscopic glass phantoms consisting of 2 um-thick rod lattices fabricated by two-photon lithography. Finally, the method is applied to brain tissue samples and compared with 3D-PLI.
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All authors Dennis Scheidt, Charlotte Voß, Cristian Rosero Arias, Santiago Saavedra Castano, Felix Matuschke, Roxana Koojimans, Katrin Amunts, Arturo Susarrey-Arce, Markus Axer

Medical Physics

HPC Modeling of Coupled Elastic-Acoustic Wave Propagation in Biological Media: Numerical Validation

Fawad Ali, Carlos García and Lapo Boschi

Accurate numerical models of sound propagation through biological media are an important tool for many applications, from medical physics to studying the auditory system of humans or other animals. We model high-frequency elastic and acoustic wave propagation through the head anatomy of a common bottlenose dolphin (Tursiops truncatus), by means of the open-source software packages specfem3d, based on the spectral-element method, and \textsc{k-wave}, based on the pseudospectral method. To achieve sufficiently high performance, we ported the latter solver to C++, for Multi-GPU CUDA support via the slab-decomposition of three-dimensional Fast Fourier Transform approach. Because the two schemes differ fundamentally in how the propagation medium is discretized and internal (in particular fluid-solid) interfaces are treated, similarity between modeled signals across methods is a legitimate measure of model accuracy. Plane waves, depending on time like four-cycle sinusoidal bursts of varying central frequency (20–100 kHz), are numerically propagated through a computed-tomography-based anatomy model using both solvers. The sound is “recorded” in front of the rostrum and at the right inner-ear locations for comparison. We successfully cross-validate both solvers on high-fidelity High-Performance Computing (HPC) clusters. We find that the stability of results from both solvers grows as the corresponding spatial resolution is refined. At their highest resolutions, the two methods show excellent agreement, with normalized correlation exceeding 0.99 across the entire 20–100 kHz frequency range. Together, our results provide a validated HPC-simulation framework for wave propagation in biological media, with broader implications, e.g., for biosonar research, auditory biomechanics, and medical ultrasound.
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Medical Physics

Fast Patient-Specific Breast CT Dosimetry: 22-Fold Acceleration of Monte Carlo MGD Estimation

Amir Entezam, Ashkan Pakzad, Christopher J. Hall et al. (+9 more)

Accurate patient-specific mean glandular dose (MGD) estimation in breast computed tomography (BCT) requires anatomically realistic models, but high-resolution patient-derived phantoms impose high computational demands on Monte Carlo (MC) dosimetry. We previously developed and validated an EGSnrc-based framework for synchrotron propagation-based phase-contrast BCT (PB-PCT) for the first-in-human study at the Australian Synchrotron. With participant imaging commencing, this study aimed to optimize phantom spatial resolution and MC efficiency while maintaining MGD accuracy, for a rapid patient-specific dosimetry. Four patient-specific heterogeneous breast phantoms with varying volume and glandularity were generated from PB-PCT images of mastectomy specimens acquired at 35 keV. The highest-resolution phantom for each specimen served as the reference. Phantoms were downsampled by increasing in-plane voxel size and slice thickness, and MGD was recalculated using EGSnrc/DOSXYZnrc. A 4.5% difference from reference MGD was adopted as the accuracy criterion. Primary photon histories were then reduced to determine the minimum required for acceptable MC precision. A common voxel size of 0.30 x 0.30 x 3.00 mm3 maintained MGD within 4.5% of reference for all four phantoms while reducing voxel number by approximately 20-31-fold. MGD was more sensitive to in-plane voxel size than slice thickness, supporting anisotropic downsampling. Reduced-resolution phantoms enabled a 15-fold reduction in photon histories to 2 x 10^8 while maintaining acceptable statistical uncertainty. Combined optimization reduced computation time from approximately 1600 to 72 min, a 22-fold acceleration. Patient-specific phantoms can be substantially downsampled while maintaining MGD accuracy and markedly reducing computational burden. This optimization provides a foundation for future real-time patient-specific BCT dosimetry.
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All authors Amir Entezam, Ashkan Pakzad, Christopher J. Hall, Anton Maksimenko, Matthew J. Cameron, Adam Round, Seyedamir T. Taba, Keith A. Nugent, Daniel Häusermann, Patrick C. Brennan, Timur E. Gureyev, Harry M. Quiney

Wednesday 2 September

Medical Physics

Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial

Phillip Chlap, Mark Lee, Trevor Leong et al. (+11 more)

Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial, the Clinical Target Volume (CTV) is complex and defined by multiple anatomical landmarks, making upfront training data preparation difficult for an automated contour QA segmentation model. We investigate anatomical priors, derived from surrounding organ segmentations, to provide spatial context and improve TOPGEAR CTV segmentation accuracy. We also evaluate active learning, iteratively expanding the training dataset by selecting cases expected to improve performance. One hundred TOPGEAR CT scans were retrospectively analyzed. An initial set of 10 expert-contoured cases was used to train an nnU-Net model. TotalSegmentator generated a voxel-wise anatomical prior map from surrounding structures as an additional input channel. Active learning was simulated over four iterations, selecting cases by model uncertainty and segmentation performance. All models used five-fold cross-validation for an ensemble uncertainty measure. Evaluation used a hold-out testing set of 50 cases. The anatomical prior improved CTV segmentation accuracy, increasing mean Dice Similarity Coefficient (DSC) from 0.84 to 0.86. Active learning similarly improved performance to 0.86, with greatest benefit in the final round. Combining the anatomical prior with active learning achieved the highest accuracy, with a DSC of 0.87. Model uncertainty correlated with DSC, supporting its use in identifying suboptimal predictions and guiding active learning. Anatomical priors and active learning each improved CTV segmentation accuracy and generalizability, with their combination achieving the best performance, supporting integration into segmentation model development for automated contour QA in radiotherapy clinical trials.
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All authors Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway

Medical Physics

Beyond Dose in Boron Neutron Capture Therapy: Cellular $^{10}$B-Capture Statistics and Microdosimetric Context in Effect Prediction

Shuichi Furuya and Atsushi Fujimura

Boron neutron capture therapy (BNCT) produces high-linear-energy-transfer, short-range particles through the $^{10}$B(n,$\alpha$)$^{7}$Li reaction. Conventional descriptors, including absorbed dose, compound biological effectiveness (CBE)-weighted dose, and photon-isoeffective dose ($D_{\mathrm{isoE}}$), remain clinically useful but compress cellular heterogeneity, compartmental boron localization, and stochastic reaction occurrence into macroscopic summaries. We propose a hierarchical framework that places cell-associated $^{10}$B-capture reaction statistics upstream of microdosimetric effect modeling and links clinical observations to cellular-scale determinants of BNCT response. We synthesize published work on BNCT microdosimetry, CBE and $D_{\mathrm{isoE}}$, stochastic microdosimetric kinetic modeling, PHITS-based cellular simulations, boron imaging, tissue microstructure, and boron-agent characterization, reorganizing these elements into observational, latent-cellular, reaction-count, and biological-effect layers. Imaging, pathology, agent-specific data, and neutron-field information constrain latent variables such as cellular boron burden, compartmental localization, and geometry; these variables determine expected reaction burdens and distributions of realized reaction counts, which are then translated into cell survival, tumor control, or normal-tissue injury by microdosimetric models. Cell-associated reaction-count statistics thus provide an interpretable inferential bridge between macroscopic measurements and established BNCT effect models, supporting spatially resolved and uncertainty-aware effect prediction without replacing existing approaches based on CBE, $D_{\mathrm{isoE}}$, SMK, or PHITS.
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Medical Physics

The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset

John T. LaMaster, Julian P. Merkofer, Dennis M. J. van de Sande et al. (+3 more)

Synthetic data is central to magnetic resonance spectroscopy method development because they provide ground truths for software validation, reproducible benchmarking, and machine- and deep-learning training. In MRSI, synthetic data must capture spatially varying anatomy, field inhomogeneity, nuisance signals, and measurement effects. The 2024 MRSI Data Processing and Quantification Challenge Synthetic Dataset is a simulated 3T brain FID-MRSI resource with ground-truth metabolite maps developed as a controlled testbed for MRSI processing and quantification methods. Subject-specific simulations used anatomical images and field maps from Human Connectome Project subjects. Tissue masks, quantum-mechanically simulated metabolite basis functions, in vivo-derived macromolecular components, Bloch-simulated post-WET residual water, registered in vivo lipid signals, spectral baseline, $B_0$-dependent frequency shifts, Voigt lineshape variations, and complex Gaussian noise were combined in a forward model. Water and lipid signals were synthesized on a high-resolution grid and Fourier-truncated to the final echo-planar spectroscopic imaging grid to model the finite spatial point spread function. This resource has 24 training and 8 testing datasets containing contaminated FID-MRSI data, anatomical images, $B_0$ maps, metadata, and ground-truth metabolite and component signals. Forward-model parameters are documented, including tissue-specific metabolite concentrations and relaxation times, macromolecular amplitude ratios, the water model, noise, and field-inhomogeneity ranges. This synthetic benchmark supports development and comparison of MRSI processing, nuisance-signal removal, and metabolite-quantification methods, while enabling method evaluation. It illustrates how characterized synthetic data can support rigorous evaluation when ground truth is difficult or impossible to obtain.
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All authors John T. LaMaster, Julian P. Merkofer, Dennis M. J. van de Sande, Brian J. Soher, Bernhard Strasser, Chao Ma

Medical Physics

Mesoscopic Light Localization and Inverse Participation Ratio Analysis of Tissue Structural Disorder for Optical Cancer Detection

Santanu Maity, Mousa Alrubayan and Prabhakar Pradhan

We introduce a mesoscopic physics-based framework that transforms conventional transmission optical micrographs into quantitative maps of tissue structural heterogeneity using the Inverse Participation Ratio (IPR). For the first time, to our knowledge, IPR-based light-localization analysis is applied to cancer tissue imaging to quantify nano- to submicron-scale structural alterations through spatial fluctuations in tissue mass density or refractive index. Unlike conventional morphology-based assessment, this physics-driven approach provides objective structural biomarkers from label-free or routinely stained tissue images. The method establishes a scalable, reproducible platform for quantitative computational pathology and enhanced cancer diagnosis by integrating mesoscopic optical physics with standard optical microscopy.
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Tuesday 1 September

Medical Physics

PyDoseRT Photon: Physics-Guided Pencil-Beam Dose Calculation with Neural Priors and Residual Correction for CT and MRI

Attila Simkó, Lukas Zimmermann, Hermann Fuchs et al. (+1 more)

We present a hybrid, physics-based analytical pencil-beam (PB) dose engine, augmented by two small frozen neural physics priors, followed by a 3-D convolutional residual-correction network (U-Net). We address the DoseRAD2026 (https://doserad2026.grand-challenge.org/) photon dose-prediction task with PyDoseRT Photon, a GPU PB engine implemented in PyTorch and corrected toward Monte Carlo (MC) accuracy in three learned stages of decreasing physical specificity. The engine reproduces the challenge's head-less MC source exactly where it can and models the patient with a beam-quality-indexed pencil kernel evaluated at the field's fluence-weighted radiological depth, and TERMA source scaling at the interaction site. Two tiny neural priors are trained through the frozen engine and then frozen themselves: a 39k-parameter 2-D fluence correction and a 48-parameter lateral heterogeneity correction mixing mass-conserving Gaussian redistribution operators. A compact 3-D U-Net (1.36M parameters) with a sequential refinement branch then predicts, per control point (CP) in the beam's-eye-view (BEV) frame, a bounded multiplicative gain and additive residual from seven channels. All learned stages are zero-initialized, so training starts from the analytical solution. For MRI, an nnU-Net regression model synthesizes a CT that enters the identical pipeline, with consecutively, the same trained corrector as the CT track. Design choices were driven by the challenge ranking, in which runtime carries double weight. The submitted method evaluated on our local CT and MR validation dataset achieved CP MAE 0.0086 and 0.0098, IDD distance 0.0011 and 0.0013, plan MAE 0.0024 and 0.0047, gamma pass rate (1%/1mm) 99.12% and 96.95%, with runtimes of 46s and 49s, respectively.
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All authors Attila Simkó, Lukas Zimmermann, Hermann Fuchs, Gerd Heilemann

Medical Physics

PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation

Lukas Zimmermann, Hermann Fuchs, Attila Simkó et al. (+1 more)

Architecture category. Hybrid method: a physics-based analytical pencil-beam (PB) dose engine followed by a 3-D convolutional residual-correction network (RepVGG-U-Net). We addressed the DoseRAD2026 proton dose-prediction task with PyDoseRT Proton, a GPU-accelerated engine implemented in PyTorch and augmented by a learned residual toward Monte Carlo (MC) accuracy. A double-Gaussian PB kernel was calibrated to GATE/Geant4 integrated depth doses in water in two stages: a classical per-energy curve fit, then a gradient-based fit of the full 3-D dose through the PyTorch physics engine as it retains a differentiable execution path for gradient-based optimization of dose-dependent objectives. The engine computes each beamlet on a beam's-eye-view (BEV) lattice with variance-preserving Gaussian splitting, an analytic nuclear halo, and a Fermi-Eyges heterogeneity term, then rotates the result into the patient frame. Additionally, a compact residual U-Net predicts an additive correction in BEV space. It is conditioned on voxelwise material-label embeddings, a discrete energy embedding and spot size. The same model was used for all anatomical sites (thoracic and abdominal). It was trained with a patient-space L1 objective emphasizing the scored high-dose region and multi-scale BEV deep supervision. The submitted CT configuration obtained preliminary-test beamlet MAE 0.0066, image-z IDD distance 0.0025, plan MAE 0.0049, 98.30% gamma pass rate (1%/1 mm), and DVH error 0.460.
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All authors Lukas Zimmermann, Hermann Fuchs, Attila Simkó, Gerd Heilemann

Monday 31 August

Medical Physics

Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation

Jakob Marcel Hoffmann, Yosuke Hasegawa and Alexander Stroh

Quantifying mitral regurgitation severity remains limited by the assumptions of clinical flow convergence methods, while high-fidelity simulation and volumetric velocimetry are too slow for routine use. We investigate whether a learned solution operator can reconstruct transient three-dimensional transvalvular hemodynamics from the sparse observation an in-vitro experiment actually provides: a single planar velocity slice and two boundary pressure traces. A Deep Operator Network is pretrained on an experimentally benchmarked URANS database spanning eleven mitral regurgitation orifice phantoms, learning a mapping from a masked two-component planar velocity snapshot to the surrounding volumetric field, and is subsequently adapted to unseen target cases by fine-tuning on their sparse measurements. Adaptation reliably corrects the flow topology within the supervised plane, reorienting a strongly eccentric jet that the pretrained operator predicts as straight, and yields full-field predictions in minutes rather than the days required by the underlying simulations. Its influence decays sharply with distance from that plane, however: measured against phase-resolved particle image velocimetry, the reconstruction error rises from 24.6% at 2mm to 52.6% at 6mm, and the resulting mismatch between corrected and uncorrected layers degrades physical consistency. Single-plane supervision thus constrains the observed plane far more effectively than the surrounding volume, which we identify as the principal obstacle to coherent 4D reconstruction from sparse planar data.
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Medical Physics

A novel vortex generator for enhancing bileaflet mechanical heart valve performance proposed through detailed hemodynamic and data-driven dynamic mode decomposition studies

A. Chauhan and C. Sasmal

Prosthetic mechanical heart valves (MHVs), particularly the bileaflet types, have been one of the most advanced and commonly implanted heart valves for more than four decades. These bileaflet mechanical heart valves (BMHVs) offer superior durability and improved hemodynamics; however, they have certain drawbacks, including the need for lifelong anticoagulation to prevent blood clot formation and the associated risks of thromboembolism. To overcome these limitations, comprehensive research studies are deployed, among which controlling the overall hemodynamics by means of passive interventions, such as placing (multiple) vortex generators (VGs) on the heart valve leaflets, is one possibility. These VGs, in particular, are known to delay the separation of boundary layers formed around the leaflet surfaces, provided they are precisely positioned and have their chosen design features. Limited research has shown that VGs improve hemodynamics beyond a BHMV when rectangular-type VGs (RVGs) are considered. Specifically, configurations such as co-rotating and counter-rotating are investigated at an incidence angle of $23^{\circ}$; however, their location on the heart valve leaflets and their shape can prove vital, an aspect that has not been explored so far. Suffice it to add, the alteration in hemodynamic performance due to these VGs may often lead to an impact on several critical clinical indices such as the pressure gradients, turbulent intensity, wall shear stress (WSS), blood damage (BD), etc., which typically establish the overall functional efficiency of an MHV.
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Medical Physics

How does human blood rheology influence arterial and cardiovascular device hemodynamics? A comprehensive review, discussion, and future directions

C. Sasmal

Human blood is a complex biological suspension whose macroscopic flow behaviour arises from the coupled microscopic dynamics of deformable blood cells, plasma proteins, and evolving cellular microstructures. As a result, blood exhibits a range of nonlinear rheological behaviours, including shear-thinning, viscoplasticity, viscoelasticity, and thixotropy, which strongly influence its physiological and clinical hemodynamics. This review provides a comprehensive assessment of the role of blood rheology in arterial and blood-contacting cardiovascular device flows, with particular emphasis on the extent to which different rheological characteristics alter clinically relevant flow quantities and on whether the conventional Newtonian approximation remains adequate for the analysis. The underlying physical mechanisms responsible for the major rheological behaviours of blood are first discussed, followed by a critical review of the constitutive models commonly employed in hemodynamic analysis, including generalised Newtonian, viscoelastic, and thixo-elasto-viscoplastic (TEVP) ones. The effects of blood rheology on arterial flows involving stenosis, bifurcation, and aneurysm under both steady and pulsatile flow conditions are subsequently reviewed and discussed in terms of several physiologically important parameters, such as pressure drop, flow resistance, wall shear stress, flow separation, and recirculation zone. The influence of blood rheology on cardiovascular devices, including prosthetic heart valves and stents, is also reviewed, particularly regarding flow stagnation, residence time, hemolysis, and thrombosis.
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Medical Physics

Clustering based magnetic assays for SARS-CoV-2 detection with scFv-functionalized magnetic nanoparticles

F. T. Wolgast, N. Lehmler, S. Westerhoff et al. (+8 more)

Magnetic nanoparticles (MNP) can be used in magnetic immunoassays (MIA) by functionalizing them with antibodies. In homogeneous assays that follow a "mix-and-measure" approach, it is then sufficient to add the sample in question and evaluate the response of the MNPs using a magnetic measurement method. Magnetic Particle Spectroscopy (MPS) is a fast and sensitive method for this purpose, capable of determining the binding state by measuring changes in Brownian relaxation. For further analysis, alternating current susceptometry (ACS) is considered, although it is significantly more time-consuming and therefore no option for applications interested in point of care detection. In this study, we aim to further improve the approach of MIA evaluated by MPS. To this end, we use self-synthesized scFv fragments instead of whole IgG antibodies during functionalization in order to keep the size of the BNF-Dextran MNP with 80 nm nominal diameter as small as possible, which allows for greater relative size changes upon binding to an analyte. Virus-like particles (VLP) and the N protein of SARS-CoV-2, which were also produced in-house, are used as analytes. Due to multiple binding sites, these form cluster structures, which, in the case of VLP, were investigated in greater depth ACS to assess their field dependence. In addition, the sensitivity of the assays was analyzed as a function of MNP concentration, and the detection limit for both analytes was estimated. We found that, using scFv-functionalized MNPs, we were able to detect low concentrations of just 490 fM of SARS-CoV-2 VLP and 1.7 nM of the N protein. However, the slightly above-proportional increase in sensitivity as the MNP concentration decreases does not automatically imply a better detection limit.
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All authors F. T. Wolgast, N. Lehmler, S. Westerhoff, S. Demiral, J. Lohmann, R. Amin, R. Sack, A. Lak, M. Schilling, M. Schubert, T. Viereck

Medical Physics

Imaging cellular-level brain microstructure with diffusion MRI

Xiaodong Li, Jing Zhao, Baolan Lu et al. (+4 more)

Noninvasive live-cell imaging in deep human tissues is crucial for exploring the cellular biological and pathogenic processes, but remains a significant unmet challenge. Diffusion magnetic resonance imaging (dMRI) promises to narrow this gap by noninvasively providing cellular-level microstructural information. Within a single crowded voxel containing millions of living cells, the intricate cellular-level microstructures create numerous microcompartments, each characterized by a specific diffusivity. However, conventional dMRI methods relying on voxel-averaged macroscopic parameters, merely reflect aggregate microstructural properties and fail to quantify this distribution of microcompartment-specific diffusivity within a voxel, thereby obscuring microstructural details. Here, we propose an intravoxel diffusivity probability distribution (IDPD) model to resolve a wealth of essential microstructural information via quantifying microcompartment-specific diffusivity distribution, thereby enabling direct cellular-level characterization. This exceptional capability is realized through a multi-tiered analytical workflow spanning targeted single-voxel or region of interest (ROI) analysis to global visualization using dynamic videos and statistic parametric maps. Ultimately, the IDPD model enables noninvasive cellular-level microstructure imaging, offering a promising avenue to evaluate living cell functions in vivo.
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All authors Xiaodong Li, Jing Zhao, Baolan Lu, Jinzhu Wang, Xinhua Wei, Qingxian Yang, Xuegang Xin

Medical Physics · Cross-listed from physics.optics

Spatially or temporally resolved phase-based optoretinography with actively-tracked point-scanning optical coherence tomography

Léo Puyo, Jonas Franke, Felix Zeisberger et al. (+2 more)

Optoretinography is a promising approach to measure visual function but its clinical translation has remained confined to research settings due to demanding hardware requirements. Here, we demonstrate that high-quality phase-based optoretinography can be performed using conventional point-scanning spectral-domain optical coherence tomography. We show that the need for high imaging speed can be circumvented by accurate sampling of phase changes at consistent spatial location. Light-evoked outer-segment expansion could be temporally resolved in B-scan series acquired at a fixed position when filtering off-location B-scans, and spatially resolved in volumes acquired at rates as low as 1~Hz. These results indicate that the technical barrier for phase-based optoretinography is substantially lower than previously assumed and pave the way toward broad clinical translation.
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All authors Léo Puyo, Jonas Franke, Felix Zeisberger, Roland Rocholz, Gereon Hüttmann