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Monday 31 August

Medical Physics

Unimodality and Radial Monotonicity of the Magnetic Resonance Fingerprinting T1/T2 Matching Objective

Ze Wang

Magnetic resonance fingerprinting (MRF) estimates tissue parameters by matching an acquired MR signal time course to entries in a Bloch- or EPG-simulated dictionary. However, no study has yet proven the uniqueness of the matching results. In two earlier studies by exhaustive objective mapping I showed that for two widely used MRF sequences the normalized-correlation objective exhibits a single dominant peak at the true T1/T2 values and decreases smoothly away from that peak. These empirical properties motivated the fast MRF-ZOOM search algorithm even without using a pre-generated signal dictionary, but their theoretical basis has remained incomplete. The purpose of this work is to develop a mathematical framework for the MRF matching objective under normalized-correlation matching.
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Friday 28 August

Medical Physics

A Reconfigurable Pipelined-SAR ADC with Embedded Compression for Temporal Compressed-Sensing Ultrasound Imaging

Reza Pakdaman Zangabad, Xitie Zhang, Levent Degertekin et al. (+1 more)

Compact ultrasound imaging systems are increasingly constrained by receiver-side sampling, conversion, memory, and data-transfer requirements. This work presents a compressed-sensing pipelined successive-approximation-register analog-to-digital converter (CS-SAR ADC) for acquisition-side temporal compression of pre-beamformed medical ultrasound radio-frequency (RF) data. Pseudo-random polarity modulation and charge-domain accumulation are embedded in the SAR sampling network so that multiple consecutive RF samples are encoded into one measurement before quantization, supporting temporal compression ratios of $N_{cT}=1$, 2, and 4. The compressed outputs are recovered off chip using a probe-specific pulse-dictionary RF model and then processed with conventional ultrasound beamforming. A 65-nm CMOS prototype was measured with a 1.2-V supply and 50-MHz master clock. In the non-compressed mode, it operates at 10 MS/s, consumes 964.49~$\mu$W, and achieves 44.12-dB SNDR and 56.40-dB SFDR for a 7.7-kHz input. The ADC output rates decrease to 5 MS/s and 2.5 MS/s for $N_{cT}=2$ and 4. Across all evaluated RF traces, median NCC values were 0.981 and 0.932, with median NRMSE values of 0.36 and 0.56, respectively. Wire-phantom localization error remained below 0.04 mm with no appreciable FWHM degradation. In the speckle-rich cyst phantom, SSIM remained 0.94 and 0.87, while CNR decreased from 3.534 in the reference to 2.047 and 1.379. These results demonstrate a hardware-realistic tradeoff in which temporal compression substantially reduces ADC conversion count and output data rate while preserving point-target geometry, whereas low-contrast cyst conspicuity is more compression-sensitive.
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All authors Reza Pakdaman Zangabad, Xitie Zhang, Levent Degertekin, Shaolan Li

Medical Physics · Cross-listed from physics.ins-det

Full-field fluorescence computed tomography (F3CT) using a calibrated virtual cone-beam pinhole geometry

Thomas Zillhardt, Yunhui Chen, Alexander Rack et al. (+4 more)

We present F3CT, a synchrotron-based hyperspectral full-field fluorescence computed tomography technique that avoids raster scanning by combining a pinhole aperture with an energy-resolving 2D detector. A virtual cone-beam model and two-stage calibration-reconstruction workflow enable 3D elemental mapping under full-field illumination. The method is demonstrated on biological and geological specimens, resolving silver-stain distributions in zebrafish tissue and high-energy fluorescence signatures in rock cores. Phase-contrast tomograms acquired sequentially under the same experimental geometry provide co-registered structural context. F3CT provides a high-throughput route to 3D XRF imaging and establishes a foundation for correlated structural and chemical tomography, with potential for future in-situ and operando implementations.
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All authors Thomas Zillhardt, Yunhui Chen, Alexander Rack, Matthew Veale, Matt Wilson, Philip J. Withers, Nicola Viganò

Thursday 27 August

Medical Physics · Cross-listed from cs.AI

PEEL: Physics-Enabled Evidential Learning for Identifiable Uncertainty in CT Imaging

Ge Wang

Normal-inverse-gamma (NIG) regression is not uniquely identifiable from its marginal Student-t likelihood: the likelihood determines three combinations of four NIG parameters and is constant along a one-dimensional fiber. We identify that fiber using independent physical measurement. As an initial embodiment, a reconstruction network receives one noisy filtered-backprojection (FBP) image and is first trained only by Student-t negative log-likelihood to estimate the three identifiable coordinates (gamma, alpha, c). The network is then frozen; repeated physical-noise realizations propagated through its reconstruction output form a Monte Carlo (MC) teacher label for output-domain aleatoric variance. An aleatoric head attached to frozen features learns this label, after which (beta, nu) are recovered algebraically. On 30 held-out simulated objects at five photon levels, one-image predictions achieved pooled Spearman correlations of 0.832-0.951 against independent 400-repeat references, median within-image correlations were 0.834-0.947, and 98.81-99.55% of evaluated pixels satisfied the algebraic admissibility condition. The method needs no KL term, reference prior, evidence regularizer, or cross-loss weight.
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Medical Physics · Cross-listed from eess.SP

A Nonuniform-Sampling Theory for Static Multi-Threshold Sampling

Ao Qiu and Qingguo Xie

Multi-Threshold (MT) sampling fixes voltage levels and records when a waveform crosses them. The resulting signal-generated event stream is governed not by a prescribed clock but by the nonuniform geometry of the realized crossing set. Within classical nonuniform sampling, we develop a retained-event MT theory for static, uniformly spaced thresholds. For a realized retained MT time set, Beurling's weak-limit theorem gives the exact necessary-and-sufficient condition for stable sampling of $PW_\Omega$. For a fixed signal class, the same principle gives the a priori criterion: uniform stable MT sampling is equivalent to Bernstein uniqueness for every set in the class weak-limit hull. This criterion reduces to computable forms under structured priors. Periodic templates give finite fiber/rank tests, periodic-gap streams give a Shannon-type complete-interpolation boundary and a stable-sampling density rule, and finite-state gap priors reduce noncritical certification to a maximum-cycle-mean condition. Kadec-type local certificates, retained-density bounds, and finite-matrix tests provide practical sufficient conditions for finite active records, while conditional perturbation bounds quantify how jitter, front-end noise, and threshold error consume a clean sampling margin under event correspondence. The theory also identifies the unavoidable boundary of this model: unrestricted finite-energy retained MT records have zero full-line lower Beurling density and therefore cannot provide a universal prior-free sampling theorem for $PW_\Omega$. Within retained-event point sampling, the MT recovery question is resolved precisely: exact recovery is governed by event-set geometry, computable guarantees require structural priors or finite-dimensional/tail assumptions, and the unrestricted static finite-energy problem is impossible.
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Medical Physics

Physics-Assisted Deep Learning Denoising for Stabilized IMPULSED dMRI Microenvironment Parameter Fitting

Wen Li, Yan Dai, Arely Perez Rodriguez et al. (+3 more)

Diffusion-weighted MRI (dMRI) is a powerful tool for quantifying cellular microenvironment parameters. This study proposes a physics-assisted deep learning (DL)-based denoising framework designed to enhance dMRI signal quality and improve the robustness of subsequent biophysical model fitting. A dataset of paired noise-free and Rician-noise-corrupted dMRI signals was generated using the IMPULSED-dMRI signal model. Three denoising architectures were evaluated: Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) networks. Denoised signals were then fitted to estimate cell diameter $d$, intracellular volume fraction $V_{\mathrm{in}}$, and extracellular apparent diffusion coefficient $D_\mathrm{ex}$. DL-based processing substantially improved dMRI signal denoising. The MLP and LSTM achieved similar performance, with the LSTM slightly better overall, and both outperformed the CNN. In the subsequent model fitting step, the LSTM produced modest reductions in parameter MAE. The dominant benefit was fitting stabilization, with the overall fitting failure rate reduced from 57.6% to 17.7%. The proposed framework improves dMRI signal quality and stabilizes subsequent IMPULSED-based microenvironmental parameter fitting.
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All authors Wen Li, Yan Dai, Arely Perez Rodriguez, Todd Aguilera, Jie Deng, Xun Jia

Medical Physics

Constrained estimation of rotational invariants of the cumulant expansion (RICE) for rapid tensor-valued diffusion MRI

Jinyang Yu, Oliver Gödicke, Frederik B. Laun et al. (+10 more)

Purpose: To complement 1.5-minute measurements of common tensor-valued diffusion MRI (dMRI) markers with rapid constrained fitting. Methods: Fast dMRI protocols for obtaining rotational invariants of the cumulant expansion (RICE) were paired with constrained weighted linear least squares (CWLLS) to stabilize the more fragile WLLS fit. A compact constraint set was formulated, including a novel mean-dependent upper bound on total diffusional variance. Evaluation used diffusion tensor distribution (DTD) simulations, healthy-volunteer data with a resolution-dependent SNR experiment, and a glioma patient dataset. A 5-minute q-space trajectory imaging (QTI) protocol served as a reference. Results: Across experiments, CWLLS reduced unphysical estimates and fit outliers in parameters such as microscopic FA and isotropic diffusivity variance. In simulations, it narrowed error distributions most clearly in the CSF-dominant case, while some metrics showed a bias-variance trade-off. In vivo, CWLLS removed negative variance estimates, truncated out-of-bounds tails, and reduced artifacts in fluid-contaminated voxels while preserving anatomical contrast. It also retained more stable maps than WLLS at higher resolution, although both estimators degraded in the lowest-SNR setting. Notably, the new mean-dependent variance bound was violated in 15.4% of voxels in the patient dataset, accounting for nearly half of the 32.7% that violated at least one constraint. Healthy-volunteer benchmarking showed that CWLLS completed in under 30 seconds. The constrained QTI fit required 72 minutes, making CWLLS 160 times faster. Conclusion: CWLLS for fast RICE yielded high-quality parameter maps at an online-ready computational cost. This may enhance the reliability of dMRI tissue characterization and strengthen the path toward clinical translation.
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All authors Jinyang Yu, Oliver Gödicke, Frederik B. Laun, Obada T. Alhalabi, Iris A. Kohler, Jürgen Hesser, Sandro M. Krieg, Bogdana Suchorska, Heinz-Peter Schlemmer, Mark E. Ladd, David Bonekamp, Johann M. E. Jende, Tristan A. Kuder

Medical Physics

Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning

Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty et al. (+1 more)

Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we introduce Dose-PlanNet, a physics-guided 3D deep learning architecture designed to predict dose distributions. This model's performance was evaluated on a cohort of patients treated in a prospective trial where two different dose fractionation regimens were employed. Dose-PlanNet achieved comparable target coverage ($D_{95}$), though statistical analysis revealed a marginal reduction in target homogeneity ($p<0.001$) offset. However the model achieved statistically significant improvements in high-dose organ-at-risk sparing ($p<0.001$). When evaluated against strict Prospective Randomized protocol volumetric constraints, automated plans met prespecified clinical acceptance criteria in $11$ out of $14$ Moderate Hypofraction Arm plans and $9$ out of $12$ Stereotactic Body Radiation Therapy Arm plans. This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy workflows while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.
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All authors Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty, Indranil Mallick

Medical Physics

Sparse Delta Integration method for the calculation of spatiotemporal pressure fields of arbitrary ultrasound transducer geometries

Deyver E. Rivera, Charlie Demene and Mickael Tanter

Accurate and efficient simulation of ultrasound pressure fields and pulse-echo responses is essential for transducer design, beamforming optimization, and model-based imaging research. Conventional Spatial Impulse Response methods compute acoustic fields by explicitly sampling trapezoidal impulse responses for each rectangular aperture subdivision, which leads to high computational cost for large apertures, dense spatial grids, and high sampling frequencies. We introduce Sparse Delta Integration, a mathematical framework for the far-field SIR of rectangular apertures that expresses the trapezoidal spatial impulse response as the double integration of a sparse set of Dirac delta distributions. This formulation yields compact expressions for time-domain and frequency-domain SIRs and enables vectorized implementations whose computational cost is independent of the trapezoid duration. We further derive a spectral formulation for pulse-echo simulation that removes part of the conventional Fourier-domain convolution pipeline. Implemented in the open-source Python package eSDIva, these methods achieve speedups of up to 180x for temporal SIR computation and up to 20x for pulse-echo RF simulation relative to Field II, while maintaining high numerical accuracy with mean squared errors below 1e-5 and correlation coefficients close to unity.
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Wednesday 26 August

Medical Physics

Impact of Multiple Non-Invasive Biosignals on Cardiovascular Biomarker Estimation via Simulation-Based Inference

Shusaku Maeda, Masahiro Nakano, Tomoharu Iwata et al. (+3 more)

As the population ages, the number of patients with cardiovascular diseases continues to increase, highlighting the need for early detection before progression to severe and irreversible functional decline. Consequently, estimating cardiovascular biomarkers from non-invasive biosignals, such as photoplethysmography (PPG) and arterial pressure wave (APW) signals, has attracted increasing attention. These signals can be measured using wearable and cuff-type devices. Previous studies have used PPG and APW signals to estimate cardiovascular biomarkers. However, these signals exhibit strong similarities in both the temporal and frequency domains and primarily reflect peripheral and arterial pulse waveforms. Therefore, they may provide limited information about cardiac mechanical function. In contrast, the quantitative impact of additional biosignals, such as ballistocardiography (BCG), which reflect the body's minute mechanical responses to cardiac ejection, remains unclear. In this study, we generated synthetic PPG, APW, and BCG signals from a unified whole-body cardiovascular circulation model and evaluated the complementary contribution of BCG to probabilistic cardiovascular biomarker estimation. We estimated posterior distributions of cardiovascular biomarkers using neural posterior estimation and simulation-based inference. The results showed that adding BCG signals significantly improved estimation performance. Furthermore, even in ill-posed cases where PPG and APW alone produced multimodal posterior distributions, adding BCG yielded unimodal posterior distributions. These findings provide fundamental insights into signal selection for estimating cardiovascular dynamics.
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All authors Shusaku Maeda, Masahiro Nakano, Tomoharu Iwata, Kenji Komiya, Ryo Nishikimi, Kunio Kashino

Medical Physics

Adapting the TG-43 formalism for use in Diffusing alpha-emitters Radiation Therapy

Guy Heger, Lior Epstein and Lior Arazi

Background: Diffusing alpha-emitters Radiation Therapy ("Alpha DaRT") enables the treatment of solid tumors using alpha particles. In Alpha DaRT, the tumor dose distribution is mainly dictated by the diffusion of the decay products of $^{224}$Ra. Due to the inherently different mechanism of dose delivery of the Alpha-DaRT source compared to sources emitting only electrons or photons, the conventional formalism of TG-43 cannot be directly applied to Alpha DaRT. Purpose: To adapt the TG-43 formalism to be used with the Alpha-DaRT model, with the specific aim of allowing commercial treatment-planning software packages to be used for Alpha-DaRT treatment planning. Methods: The effective dose rate is defined, along with an activity conversion factor. These, together with a numerical calculation that solves the model equations, allow implementing the Alpha-DaRT model predictions into the TG-43 formalism. Results: An example of calculated $F(r, \theta)$ and $g_L(r)$ tables is presented for an Alpha-DaRT treatment scenario, with the model parameters representing preclinical data on squamous cell carcinoma. Conclusions: Commercial software can be used for Alpha-DaRT treatment planning. However, the generated $F(r, \theta)$ and $g_L(r)$ tables depend on tissue parameters, as well as treatment duration, and so these tables should be generated on a case-by-case basis.
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Medical Physics · Cross-listed from cs.CV

PAGS: Autofocusing Photoacoustic Tomography via Speed-of-Sound-Adaptive Gaussian Splatting

Jiarui Ge, Jintao Ma, Bangxu Fan et al. (+4 more)

Photoacoustic computed tomography (PACT) combines optical absorption contrast with acoustic detection for high-resolution deep-tissue imaging. A persistent challenge is that unknown speed-of-sound (SoS) heterogeneity changes acoustic time-of-flight, causing defocusing artifacts when reconstruction assumes a uniform SoS. Existing SoS-adaptive methods either rely on calibrated acoustic priors or optimize dense physical medium models, which becomes expensive and difficult to scale in 3D. We propose PAGS, a differentiable framework for blind autofocusing PACT via speed-of-sound-adaptive Gaussian splatting. PAGS represents the initial pressure field with sparse Gaussian photoacoustic (PA) sources and replaces explicit medium recovery with a compact anisotropic path-averaged SoS (ASoS) field parameterized by spherical harmonic probes. This latent propagation field directly controls source-to-transducer arrival-time alignment, while an analytic Gaussian acoustic projection maps the source representation to transducer signals efficiently. The resulting closed-loop signal-domain optimization jointly updates the Gaussian PA source parameters and the ASoS field from measured data, without calibrated SoS priors. Experiments on simulated and physical phantom data demonstrate improved reconstruction sharpness under heterogeneous acoustic media, robustness to sparse-view sampling, and computational benefits from the analytic Gaussian projection.
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All authors Jiarui Ge, Jintao Ma, Bangxu Fan, Jinyan Zhang, Xiaokang Yang, Shuai Na, Xiaoyun Yuan

Medical Physics · Cross-listed from physics.optics

Nanophotonic control of spatial information in scintillation detectors

Joshua Chen, Simo Pajovic, Seou Choi et al. (+9 more)

X-rays enable non-invasive imaging across medicine, security, materials science, and beyond, yet modern systems remain constrained by the need to resolve finer structures at lower radiation dose. Scintillators are the dominant materials for detecting X-rays, but face a longstanding compromise: thick scintillators absorb X-rays effectively, whereas optical photons generated throughout their volume spread before detection, degrading spatial information. Existing scintillator architectures largely try to preserve resolution by physically confining light using pixels, columnar crystals, or microstructured channels. Here, we show that high-resolution detection does not require the volumetric confinement of scintillation light. A metalens integrated directly with a bulk scintillator uses nanophotonic wavefront control to preferentially transfer high-spatial-frequency information from volumetrically generated scintillation light to the detector, while retaining the X-ray absorption of a thick scintillator. We experimentally recover fine spatial detail in X-ray images of inorganic and biological specimens. In a detector geometry relevant to computed tomography (CT), the experimentally validated model predicts a fivefold reduction in required X-ray dose and a 25-fold increase in resolution bandwidth relative to a state-of-the-art pixelated scintillator. These results establish wavefront engineering as a route to separating efficient X-ray absorption from optical image formation, with the potential for substantially higher-resolution, lower-dose CT.
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All authors Joshua Chen, Simo Pajovic, Seou Choi, Sachin Vaidya, William Michaels, Louis Martin-Monier, Christina M. Spägele, Steven E. Kooi, Juejun Hu, Rajiv Gupta, Charles Roques-Carmes, Marin Soljačić

Tuesday 25 August

Medical Physics

Evaluation of the Exradin A30 Parallel Plate Ion Chamber as a Reference Dosimeter in Ultra-High Dose Rate (UHDR) Electron Beams

Mervat Alharbi, Kevin Liu, Brian Hooten et al. (+8 more)

Reliable reference dosimetry for ultra-high dose-rate (UHDR) beams (>40 Gy/s) is challenging because conventional ionization chambers (ICs) exhibit saturation from ion recombination. The Exradin A30 IC uses an ultra-thin 0.3-mm electrode spacing to improve charge-collection efficiency (CCE). This study evaluated the commercial A30 as a reference dosimeter for UHDR electron beams by characterizing leakage current, CCE, polarity correction (Ppol), and beam-quality correction factors (kQ). Measurements were performed with a 9-MeV IntraOp Mobetron from the accelerator head, achieving up to 9 Gy per pulse (DPP) and an instantaneous dose rate of 2.25 MGy/s. Data were acquired in grounded water-equivalent plastic, distilled water, and saline water. DPP was varied by changing SSD at a fixed 4-{\mu}s pulse width, while pulse repetition frequency (PRF) ranged from 5 to 90 Hz. CCE was determined using EBT-XD film under matched UHDR and conventional dose and energy conditions. CCE and Ppol were also evaluated as functions of DPP and PRF in distilled and saline water. Values of kQ were calculated using Monte Carlo simulations and measured in TrueBeam electron beams. Leakage current was <2 fA. Both CCE and Ppol decreased with increasing DPP; however, CCE remained 90-99% across all three phantoms, while Ppol decreased from 0.990 to 0.981 in liquid and solid water. Neither CCE nor Ppol depended on PRF over 5-90 Hz. Measured and calculated kQ values agreed within 0.8% at all energies except 9 MeV, where they differed by 2%. The A30 exhibited 5% recombination at DPP up to 5 Gy in distilled and saline water. Its response in solid phantoms was affected by charge buildup, which was mitigated by grounding. With appropriate CCE corrections and grounded solid phantoms, the commercial A30 is suitable for reference dosimetry in UHDR electron beams.
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All authors Mervat Alharbi, Kevin Liu, Brian Hooten, Shannon Holmes, Stefanno Alarcon-Nunez, Jeffrey Radtke, Larry DeWerd, Sam Beddar, Wesley Culberson, Emil Schueler, Ahtesham Khan

Medical Physics

Clustering of anterior corneal surface shapes in normal adults

Hala Bouazizi, Isabelle Brunette and Jean Meunier

In the present study, we investigated a large dataset of normal adult corneal topographies in an exploratory attempt to identify the natural groupings of their 3D shapes, with the practical clinical aim of categorizing the shape of a future generation of biosynthetic corneal implants. Because of the wide range of shapes among normal corneas, we needed to identify a limited number of normal corneal shape categories to guide the conception of different implants to match the patient's own corneal shape category. At this stage of our research, we focused on the anterior surface, which exhibits greater variation in a normal population and is responsible for most of the refractive power of the eye. The corneal surface 3D data used were the anterior elevation maps taken from corneal topographies. Clustering corneal topography elevation maps was performed to reveal the normal corneal shape categories. To facilitate groupings, corneal data had their dimensionality reduced by Zernike polynomial modeling. The resulting clusters were evaluated using different clustering scores, representations and statistical cluster comparisons. While several hard and soft linear and nonlinear clustering methods were tested in this way, k-means proved to be sufficient for this task. The best number of clusters was three and they were primarily differentiated according to corneal curvature (Zernike defocus coefficient). These clusters were related to established clinical parameters, confirming that the algorithm extracted relevant information based solely on the normal 3D shape of corneas.
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Medical Physics · Cross-listed from physics.optics

Effects of Quantum Noise and Source Blurring on Dark-Field Signal Retrieval in X-ray Speckle-Based Imaging

Hunwoo Lee, Jingcheng Yuan and Mini Das

X-ray speckle-based dark-field imaging offers high sensitivity to sub-pixel structural features, yet its quantitative reliability in clinical and preclinical settings remains constrained by low photon flux and finite focal spot sizes. However, how hardware-induced noise and source blurring propagate through retrieval algorithms to degrade signal integrity is not fully understood. Here, we systematically evaluate algorithm robustness quantified by signal linearity, sensitivity, and bias under photon starvation and source blurring across two mathematically distinct frameworks: differential-based intrinsic tracking (Low-Coherence System, LCS) and patch-wise explicit tracking (X-ray Speckle-Tracking Speckle-Vector-Tracking, XST-XSVT). Our experimental results demonstrate that input speckle pattern distortions propagate through retrieval algorithms in fundamentally different ways depending on algorithm architecture. As an example, using our setup, under severe photon starvation, derivative noise amplification in LCS causes its dark-field signal linearity and sensitivity to drop precipitously, while sharply elevating baseline bias. In contrast, XST-XSVT restricts these losses for sensitivity while maintaining a stable baseline, as its patch-wise variance calculation inherently suppresses stochastic noise. Similarly, under blur-limited conditions , source blurring washes out the speckle pattern, directly reducing dark-field sensitivity for both LCS and XST-XSVT. This characterization establishes operational boundaries for low-power and low-coherence X-ray systems, guiding algorithm selection and framework optimization to realize quantitative dark-field imaging in preclinical and clinical applications.
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Medical Physics · Cross-listed from physics.optics

Metalens stereoscopic optical palpation for imaging cancer mechanics

Haoyi Yu, Rhys Jones, Chi Li et al. (+13 more)

Accurate intraoperative margin assessment is critical for complete resection of solid tumours. However, surgeons routinely rely on manual palpation, which is highly subjective and fundamentally limited by poor spatial resolution, and pre-operative imaging lacks real-time feedback. While emerging optical elastography techniques offer quantitative mechanical contrast, their reliance on bulky optical components necessitates cumbersome alignment and hinders integration into miniaturised surgical instruments. Here, we report metalens stereoscopic optical palpation (MSOP), a nanophotonic imaging platform leveraging a binocular metalens integrated with a single CMOS sensor to achieve high-fidelity, self-aligned elastography. By replacing complex bulk optics with a compact stereoscopic architecture, MSOP enables robust, high-contrast mapping of tissue surface stress. Following validation in heterogeneous silicone phantoms, we demonstrate MSOP's translational potential by characterising the mechanical signatures of malignancies in fresh mouse pancreatic cancer models, as well as excised human breast and liver specimens. By providing reliable, label-free mechanical contrast across diverse oncological landscapes, MSOP offers a compact tool for precise intraoperative margin delineation to reduce re-excision rates and improve surgical outcomes.
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All authors Haoyi Yu, Rhys Jones, Chi Li, Bo He, Renate Zilkens, Louise N. Winteringham, Laura Gale, Mireille Hardie, Fiona Bellamy, Saud Hamza, Christobel M. Saunders, Liver Cancer Collaborative, Stefan A. Maier, Brendan F. Kennedy, Haoran Ren, Qi Fang

Medical Physics

Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis

Cyrus Mexon Evrard Djindot, Faliang Liu, Sylvain Laborde et al. (+6 more)

Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincaré indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.
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All authors Cyrus Mexon Evrard Djindot, Faliang Liu, Sylvain Laborde, Yinjia Zhang, Jessie Chen, Ming Li, Congrong Wang, Weixiong Rao, Qinpei Zhao

Medical Physics · Cross-listed from physics.bio-ph

Quantifying the Biophysical Properties of Red Blood Cells in Gaucher Disease

Zhaojie Chai, Marine de Person, Pierre A. Buffet et al. (+2 more)

Gaucher disease (GD), the most common lysosomal storage disorder, alters red blood cell (RBC) mechanics and circulation, contributing to vascular occlusions, bone infarcts, and splenomegaly. However, the individual roles of GD-RBC biophysical properties in these processes remain unclear. Here, we present a combined computational-experimental investigation to quantitatively characterize GD-RBC biophysical properties and determine how specific mechanical parameters drive abnormal RBC behavior. Informed by experimental data, we independently quantify key RBC properties, including shear modulus (mu), surface-to-volume ratio (S/V), and bending modulus (k_c). Based on these parameters, we construct three GD-RBC subtypes (GD-RBC1-3) to systematically isolate their individual contributions. At the single-cell level, optical tweezers simulations show up to ~27% reduction in axial diameter and ~42% reduction in transverse compression. Tank-treading dynamics exhibit non-monotonic behavior, with rotation frequencies increasing by up to ~70% or decreasing under elevated bending rigidity. In confined flow, traversal times through microchannel constrictions increase by more than a factor of two, while splenic slit passage times rise from ~250 ms (control) to >1200 ms for the severe GD-RBC subtype, approaching a functional no-passage threshold. At the population level, viscosity simulations demonstrate that these alterations collectively elevate blood viscosity, with small fractions (~4.0%) of highly rigid cells disproportionately increasing flow resistance. Overall, this study provides a quantitative and mechanistic framework that disentangles the contributions of key RBC parameters to abnormal behavior in GD, linking cellular-scale biophysics to hematologic dysfunction and microvascular occlusion.
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All authors Zhaojie Chai, Marine de Person, Pierre A. Buffet, Melanie Franco, George Em Karniadakis

Monday 24 August

Medical Physics · Cross-listed from cond-mat.mtrl-sci

The Achilles tendon enthesis rebuilds its mineralization front on reloading but retains a nanoscale imprint of unloading

M.L. Stammer, C. Camy, M. Frewein et al. (+16 more)

The enthesis is a graded fibrocartilaginous interface that transfers load between tendon and bone, yet the nanoscale mechanisms stabilizing its mineralization front remain unclear. Here, we combine multimodal 2D/3D X-ray imaging with nonlinear optical microscopy to map structural, crystalline and extracellular matrix organization across the murine Achilles tendon enthesis under unloading and reloading. Unloading reduces the tidemark-associated two-photon fluorescence (2PF) peak and is accompanied by diffuse mineralization into previously unmineralized fibrocartilage. This unloading-associated mineral exhibits increased apparent crystallite size, an enlarged c-axis lattice parameter, reduced crystalline texture and a diminished collagen order gradient, consistent with an altered mineralization environment. Upon reloading, the 2PF peak recovers, but a new tidemark forms ~20 um from the original boundary, creating a zone with a persistent nanoscale imprint in the mineral tessellation. These findings establish the enthesis as a mechanically governed graded interface in which matrix-mediated boundary control constrains mineral formation and in which a record of mechanical history is imprinted into the nanostructure.
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All authors M.L. Stammer, C. Camy, M. Frewein, I. Silva Barreto, C. Genovesio, M. Eckermann, A. Karimbana, K. Iliopoulos, R. Ranjan, N. Wittig, T. Fovet, T. Brioche, A. Chopard, M. Burghammer, S. Brasselet, H. Birkedal, M. Pithioux, S. Roffino, T.A. Grünewald

Sunday 23 August

Medical Physics

Pulse-Echo Ultrasound Methods for Speed-of-Sound Estimation: A Review

Can Deniz Bezek and Orcun Goksel

Speed of sound (SoS) is fundamental to ultrasound beamforming and a promising quantitative biomarker for tissue characterization. Although transmission- and reflector-based methods have demonstrated the diagnostic value of SoS, their need for dedicated hardware or constrained acquisition geometries limits clinical usability. Pulse-echo SoS imaging addresses these limitations by estimating SoS with conventional ultrasound probes and data acquisition paradigms. This review provides a structured overview of pulse-echo SoS estimation methods, categorized by the source of information used, the estimated SoS representation, and the reconstruction strategy. We review methods by their locality, e.g., global, layer-wise, and spatially-resolved predictions, as well as in terms of prediction strategies including gradient-free search, optimization-based solutions, analytical closed-form approximations, and deep learning-based methods. Finally, we summarize emerging diagnostic applications in the breast, liver, and muscle, while discussing challenges and possibilities with SoS imaging.
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Medical Physics

Shimmer: End-to-End Open-Source Passive B0 Shimming Methodology for Low-Field MRI Magnets

Ilia Kulikov, David Schote, Helge Herthum et al. (+7 more)

Low-field MRI scanners based on permanent magnet arrays require accurate B0 shimming to achieve sufficient field homogeneity for imaging. Here, we present Shimmer, an open-source methodology for passive shimming of low-field MRI magnets using additional NdFeB magnets placed outside the main magnet array. The end-to-end methodology combines magnetic field mapping, magnetic field simulations, numerical optimization of shim magnet positions and orientations, and automated generation of 3D-printable shim holders. Continuous magnet rotations and several optimization strategies are considered and evaluated. The method was validated and applied to three different permanent magnet arrays ~50 mT in 200 mm diameter spherical volumes, reducing initial field inhomogeneities of 9361ppm, 31721 ppm and 34143 ppm to 888 ppm, 1812 ppm and 1602 ppm respectively. The improved homogeneity enabled undistorted MR imaging of a phantom. Shimmer provides a reproducible and adaptable approach for improving the performance of low-field MRI magnets.
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All authors Ilia Kulikov, David Schote, Helge Herthum, Martin Häuer, Kimon Hadjikiriakos, Jan Gregor Frintz, Sonja Buxbaum-Conradi, Manuel Moritz, Tobias Redlich, Lukas Winter

Saturday 22 August

Medical Physics

A novel strategy for achieving a low-field lightweight permanent MRI magnet system with good magnetic field homogeneity and low eddy current

Jingzhi Han, Jiangqian Guo, Peng Shen et al. (+10 more)

In low-field, lightweight, pole-pieceless permanent-magnet MRI systems built with sintered Nd-Fe-B or Sm-Co magnets, the rapid switching of gradient fields readily induces eddy currents in the sintered magnets, leading to image artifacts. To address this, we report for the first time a Sm-Fe-N permanent-magnet MRI system based on anisotropic Sm-Fe-N bonded magnets, whose high electrical resistivity reduces the eddy currents in the X, Y and Z directions to 0.093%, 0.172% and 2.38%, respectively, while a magnetic field inhomogeneity below 150 ppm is achieved at the boundary of a 220 mm diameter of spherical volume (DSV). Compared with sintered Nd-Fe-B and Sm-Co magnets, using Sm-Fe-N bonded magnets as the source of the static magnetic field not only suppresses eddy currents but also makes a closely tiled, densely packed magnetic-circuit layout feasible, providing a more uniform static magnetic field for the MRI system. Imaging results free of obvious geometric distortion and banding artifacts further indicate that the Sm-Fe-N magnet system delivers low eddy currents and high static magnetic field homogeneity.
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All authors Jingzhi Han, Jiangqian Guo, Peng Shen, Xiao Tong, Wenyun Yang, Ziheng Zhang, Jie Liu, Tianzhuo Yang, Yikun Fang, Shunquan Liu, Jie Zhang, Qing Xu, Jinbo Yang

Medical Physics

A scattering correction method for CT reconstruction based on the Wavelet Adaptive Material-dependent Boltzmann Transport Equation (WAM-BTE)

Huiying Pan, Xu Jiang and Xing Zhao

X-ray computed tomography (CT) is an essential imaging technology in clinical diagnosis. However, scattered photons can reduce image contrast and introduce CT value bias, which severely degrades image quality. Recently, scatter correction methods based on the Boltzmann transport equation (BTE) have attracted increasing attention due to their high physical accuracy and flexibility. Nevertheless, existing BTE-based methods usually employ single-material models, which cannot accurately describe the nonlinear energy dependence of photon interaction cross-sections in different materials. In this work, a scatter correction method based on the wavelet adaptive material-dependent BTE (WAM-BTE) is proposed. The conventional single-material model is extended to a multi-material model by introducing material-dependent scattering distributions. Furthermore, an adaptive multi-scale framework is established through wavelet decomposition. The low-frequency wavelet coefficients are used for coarse-scale scatter estimation to reduce computational complexity, while the high-frequency wavelet energy of high attenuation materials is utilized for adaptive local refinement. Theoretical analysis demonstrates that, when using the Haar basis function, the low-frequency wavelet coefficients at the $w$-th level are mathematically equivalent to block-average downsampling with a scale factor of $2^w$, except for a deterministic normalization factor. Experimental results show that the proposed WAM-BTE method achieves comparable accuracy to the Monte Carlo method while preserving the computational efficiency of coarse-scale estimation. The scatter calculation time for a single projection view is reduced to the millisecond level.
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Friday 21 August

Medical Physics

Topogram-Gated Multi-Modal Pseudo-CT Synthesis for PET/MR Attenuation Correction

Joris Wuts, Jakub Ceranka, Vicky De Ridder et al. (+1 more)

As part of the Big Cross-Modal Attenuation Correction Challenge (BIC-MAC), we aimed to synthesise pseudo-CT images for PET/MR attenuation correction from whole-body non-attenuation-corrected PET (NAC-PET), Dixon MRI, and a two-dimensional topogram image. We developed a residual 3D U-Net in which a dedicated 2D topogram encoder provides bounded, multi-scale feature gating. The network was supervised jointly in CT and 511-keV attenuation-map space. Two complementary models, one emphasizing activity and cranial regions and one including a projection-domain attenuation-correction-factor regulariser, were combined by equal averaging in CT space. In controlled development experiments, the two training objectives yielded complementary error profiles. On the online BIC-MAC validation leaderboard, the ensemble achieved a mumap MAE of 0.005656, whole-body SUV MAE of 0.0356, organ bias of 2.65%, and brain outlier score of 0.0294. Topogram-gated multi-modal learning combines local volumetric information with global projected anatomy and provides a practical, physically informed approach to pseudo-CT synthesis for PET/MR.
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All authors Joris Wuts, Jakub Ceranka, Vicky De Ridder, Jef Vandemeulebroucke

Medical Physics · Cross-listed from eess.IV

Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning

Rory Bell, Artemis Bouzaki, Jiaming Cao et al. (+2 more)

We participated in the BIC-MAC Challenge with a multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using separate PET and MR encoders, multi-scale feature fusion, and FiLM-based topogram conditioning at the bottleneck, we obtain a model that integrates complementary cross-modal information while reducing reliance on precise voxel-wise correspondence between modalities. Our final submission can be found: https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026
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All authors Rory Bell, Artemis Bouzaki, Jiaming Cao, Jasmine Morrison, Chelsea Sargeant

Thursday 20 August

Medical Physics · Cross-listed from eess.IV

Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising

Merve Gülle, Junno Yun, Yaşar Utku Alçalar et al. (+1 more)

Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensive iterative refinement, limiting their practical deployment. Consistency models (CMs) provide a compelling alternative, aiming to map out the diffusion trajectory in a single pass, enabling faster generation. In this work, we propose CM-RED, a novel MRI reconstruction method that integrates a pretrained CM into the regularization by denoising (RED) scheme. Our method builds on accelerated proximal gradient RED (RED-APG), and further incorporates controlled noise injection during the update steps to enhance generative diversity and accelerate convergence. Extensive experiments on the fastMRI knee and brain datasets demonstrate that CM-RED achieves high-quality reconstructions across multiple anatomies, contrast weights, acceleration factors, and undersampling patterns, using only 4 network function evaluations (NFEs). The proposed method consistently outperforms existing DM- and CM-based approaches in both quantitative metrics and visual fidelity, and exhibits strong robustness to hyperparameter variations, highlighting CM-RED as an efficient and effective generative framework for accelerated MRI reconstruction. The source code and pretrained models are publicly available at https://github.com/MerveGulle/CM-RED.
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All authors Merve Gülle, Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya

Medical Physics

An integrated diffusion-weighted imaging processing and interpretation platform for MR-guided radiotherapy

Yunxiang Li, Yan Dai, Yen-Peng Liao et al. (+3 more)

Background: Magnetic resonance imaging-guided linear accelerators (MR-Linacs) allow diffusion-weighted imaging (DWI) to be acquired at every treatment fraction, but converting these low-signal-to-noise-ratio acquisitions into clinical decisions requires both reliable quantitative processing and an interpretation that reconciles a scattered and often contradictory literature. Purpose: To describe and evaluate an integrated, web-based platform that carries raw MR-Linac DWI to a structured, literature-grounded clinical interpretation, and to assess its retrieval-augmented generation (RAG) interpretation module by independent expert rating. Methods: The platform couples a deep-learning processing pipeline, comprising distortion correction, denoising, and intravoxel incoherent motion (IVIM)/apparent diffusion coefficient (ADC) fitting, with longitudinal region-of-interest analysis and a RAG interpretation agent. The agent reasons over a two-layer knowledge base of curated publications (a structured catalog index plus line-indexed full text), delegates arithmetic to deterministic tools, and is designed to trace each statement to a source document, section, and line range. One medical physicist and one physician independently rated the agent's reports for nine longitudinal glioblastoma cases on a 1-5 scale across three metrics: clinical-reasoning soundness, literature-citation quality, and overall clinical utility. Results: Across 54 ratings, the pooled mean was 4.65 +/- 0.80, with 93% of ratings >= 4; metric means were 4.6 (reasoning), 4.5 (citation), and 4.8 (utility), and raters agreed within one point on 85% of paired ratings. Conclusions: A single platform can integrate MR-Linac DWI post-processing with traceable, expert-evaluated clinical interpretation, while highlighting the safeguards needed to verify LLM-generated reasoning in radiation oncology.
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All authors Yunxiang Li, Yan Dai, Yen-Peng Liao, Jie Deng, Jill B De Vis, You Zhang

Medical Physics · Cross-listed from eess.IV

Flow Matching-Based PET Image Reconstruction

Fumio Hashimoto, Ziqian Huang, Tatsuya Yokota et al. (+1 more)

Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstruction methods have demonstrated promising performance, they often require many reverse sampling steps with data-consistency updates incorporated into the sampling process. Flow matching offers an attractive alternative because it can directly estimate clean images from intermediate states, allowing data-consistency refinement to be separated from flow propagation. In this work, we proposed flow matching-based PET image reconstruction methods. We first established PET-FlowDPS by incorporating Poisson likelihood guidance with an expectation-maximization (EM)-based preconditioner into the FlowDPS framework. We then proposed a model-based PET reconstruction method that used a pretrained flow matching model as a prior, in which the flow-based prior, PET data refinement, and stochastic propagation were interpreted within an approximate Bayesian framework. Experimental results using [$^{\text{18}}\text{F}$]FDG brain PET datasets showed that the proposed method achieved better bias-variance trade-offs across different dose levels compared with other reference methods. These results demonstrated the potential of flow matching as a generative prior for quantitative PET image reconstruction.
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All authors Fumio Hashimoto, Ziqian Huang, Tatsuya Yokota, Kuang Gong

Medical Physics

Simultaneous 3D co-registered perfusion and oxygenation with ULM, photoacoustic imaging, and a planar matrix array

Léa Davenet, Jacques Battaglia, Franck Lager et al. (+6 more)

Objective. Joint assessment of tissue oxygenation and microvascular perfusion could offer valuable insights into vascular function across a wide range of biomedical applications. Multispectral photoacoustic imaging enables the evaluation of blood oxygenation, while ultrasound localization microscopy provides sub-diffraction visualization of the microvasculature and blood perfusion. Here, we combine these two complementary modalities to simultaneously generate co-registered, volumetric maps of blood oxygenation and perfusion. Approach. Photoacoustic imaging and ultrasound localization microscopy are both ultrasound-based techniques. We developed an imaging platform that integrates the two modalities using a single planar ultrasonic matrix array, a state-of-the-art array for 3D ultrasound localization microscopy. The bimodal platform was validated in vitro using vessel-mimicking phantoms, then in vivo in mice. Main results. In vitro bimodal images of tubes injected with contrast agents demonstrated a coregistration accuracy of 20 $\mu$m and revealed complementary structural and functional information. Multispectral photoacoustic imaging achieved oxygen saturation measurements spanning the physiological range (60-95 %) with 5 % accuracy using only five optical wavelengths. In vivo imaging of healthy mouse tissues with known vascular anatomy further demonstrated the ability of the proposed platform to jointly characterize blood oxygenation and microvascular perfusion. Significance. This work experimentally validates a bimodal photoacoustic imaging-ultrasound localization microscopy approach using a planar ultrasound array. We characterized the functional imaging performance of this platform and identified limited-view artifacts inherent to this array configuration in photoacoustic imaging. These findings establish a foundation for adopting the platform in future studies of murine models and for advancing this promising bimodal approach.
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All authors Léa Davenet, Jacques Battaglia, Franck Lager, Pascal Dargent, Charlotte Lussey-Lepoutre, Bertrand Tavitian, Olivier Couture, Lori Bridal, Jérôme Gateau