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Saturday 3 October

Medical Physics

A Guidewire Trajectory Generation Tool for Interventional MRI Simulations

Fatima Ben Haj, Jeffrey Hand, Shaihan Malik et al. (+1 more)

Significance: RF-induced heating of endovascular devices limits MRI-guided interventions, including targeted drug delivery to brain tumors. Purpose: To present a tool that rapidly generates randomized guidewire trajectories for RF heating risk assessment. Methods: A MATLAB tool models the guidewire as a spline with randomized control nodes and a fixed nominal length. As a use case, tip sSAR was estimated for 1000 trajectories using straight-wire transfer functions and 64 MHz local and body coil fields (matched B1) in the Duke phantom. Results: Length accuracy was 99.84%, and 3000 trajectories were generated in 11.5 s. Body-to-local sSAR ratios at the 50th, 95th, and 99th percentiles were 1.5, 1.6, and 2.27, but some trajectories heated more under the local coil. Conclusion: The tool enables large-sample heating assessment, and coil type alone did not reliably predict individual trajectory risk.
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All authors Fatima Ben Haj, Jeffrey Hand, Shaihan Malik, Felipe Godinez

Thursday 1 October

Medical Physics · Cross-listed from cs.CV

MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI

Negin Kafee Hernashki and Soumick Chatterjee

Unsupervised anomaly detection (UAD) methods for brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how each anomaly map is aligned with the reference, how and on which data the threshold is set, and which false-positive budget, metric, aggregation and lesion definition are used. We present MIRTO, an evaluation protocol that makes these choices explicit and measures their effect. It gates the geometry of every comparison with a registration check and label-free diagnostics of known power, sets thresholds on validation data alone and reports the false-positive volume actually realised on test, repeats each comparison over 15,552 defensible evaluation pipelines, and attaches paired subject-bootstrap intervals with multiplicity control. Applied to four UAD methods trained on the same healthy data and tested on 312 BraTS 2020 subjects, MIRTO showed that an axis-order mismatch between stored maps and the reference lowered a diffusion model's voxel AUROC from 0.873 to 0.583 whilst barely moving its slice-level AUROC. Within each metric, the method explained at least 0.95 of the variance in voxel AUROC and AUPRC and 0.77 in Dice, but only 0.14 in lesion sensitivity, where the lesion definition and hit criterion dominated. A Dice advantage that was significant at validation thresholds vanished at equal realised false-positive burden, and an exact identity attributes it to threshold transfer. A training-free change to REFLECT's latent aggregation raised Dice at equal burden by 0.052. Nine hypotheses were tested against explicit criteria; because the same cohort served to develop the protocol, all inference is exploratory.
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Medical Physics

Diffusion broadening of the point spread function in steady-state MRI

Bibek Dhakal and John C. Gore

Purpose: To demonstrate how diffusion blurs the longitudinal magnetization in steady-state gradient-echo imaging, quantify resolution dependence on flip angle and repetition time, and the effects on relaxometry at microscopic resolution. Methods: Diffusion was incorporated into the Bloch-Torrey equation in $k$ space to determine how the point spread function (PSF) of $T_1$-weighted spoiled gradient-echo images varies with flip angle. Theoretical predictions were validated numerically and extended to b-SSFP. Results: Diffusion acts as a low-pass filter whose width depends on the diffusion rate ($D$), repetition time (TR), $T_1$ and flip angle ($\alpha$). The blurring length spans from $\sqrt{D \cdot \mathrm{TR}/2}$ at large $\alpha$ to $\sqrt{D T_1}$ as $\alpha \rightarrow 0$, equaling $\sqrt{D T_1/2}$ at the Ernst angle. Consequently, multi-angle acquisitions have mismatched resolution (PSF widths of 39 $\mu$m at $\alpha = 10^\circ$ versus 11 $\mu$m at $\alpha = 80^\circ$ for TR = 50 ms). For the flip-angle pair optimizing variable-flip-angle $T_1$ mapping, the effective resolutions differ by a factor of 2.4. In simulated DESPOT1 maps the $R_1$ of a 30 $\mu$m feature is reduced to 1.80 s$^{-1}$ (true value 2.00 s$^{-1}$). In b-SSFP, blurring depends on $T_1$, $T_2$, and $\alpha$ but is independent of TR, with a minimum of $\sqrt{D T_2}$. Conclusion: The PSF of a steady-state sequence depends on the flip angle and $T_1$. Combining images acquired at different flip angles implicitly assumes identical spatial resolution, but that is not valid at microscopic scales, affecting the accuracy of quantitative relaxometry. The theory identifies a new limitation on the ultimate resolution of MRI set by diffusion.
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Medical Physics

Towards 3D fully randomized frequency-domain reconstruction of the speed of sound in breast ultrasound computed tomography

Luca A. Forte

Ultrasound computed tomography is emerging as a promising diagnostic imaging tool. 2D geometries suffer from notorious out-of-plane scattering artifacts. Image reconstruction can be achieved with frequency-domain full waveform inversion and it can be further accelerated by randomly phase-encoding the elementary sources. In this manuscript, we extend our previous results for the 2D geometry of a ring-array to the 3D cylindrical geometry of multiple rings. In particular, we consider the cases of an elementary source described by a single array element (quasi-omni-directional transmission), a line source and a focused transmission respectively. With differences in image quality, we prove that a fully randomized frequency-domain inversion in 3D is capable to reconstruct portions of a human breast surrounded by the cylindrical geometry and detect mm-size masses of varying contrast in dense breast, in reasonable computing times, thus opening the concrete possibility to the design of 3D imaging devices with high sensitivity levels. The methods are applicable to multiple tomographic geometries in 3D and, in principle, can be integrated into next generation medical ultrasound scanners.
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Medical Physics

AnatomIQ: An Open-Source Toolkit for Automated Background Detection in Medical Imaging

Rafael Carballeira, Hayley A. Cash and Marthony L. Robins

Manual background selection for contrast-to-noise ratio (CNR) calculations in CT image quality assessment is time-consuming, operator-dependent, and compromises reproducibility. Advanced metrics such as Noise Power Spectrum (NPS) and Task Transfer Function (TTF) traditionally require dedicated phantom acquisitions, adding cost, radiation exposure, and workflow disruption. We developed AnatomIQ, an open-source Python toolkit that automates background detection and performs phantom-free NPS and TTF analysis directly on patient anatomy. Automated background detection uses tissue-specific Hounsfield unit thresholding and morphological filtering, paired with a web interface for click-to-select lesion identification from PACS screenshots. Reported metrics include CNR, signal-to-noise ratio, detectability indices, and dose-normalized figures of merit with automated optimization recommendations. NPS quantifies noise texture from automatically detected uniform tissue regions, while TTF leverages natural circular anatomical structures (e.g., trachea, vessels) as edge phantoms, together characterizing the resolution-noise trade-off without dedicated phantom scans. Analysis takes 2-3 minutes per case, with CNR computed in under 10 seconds and NPS/TTF available on demand. Automated detection reliably identified valid regions across anatomical sites, including head-and-neck scans, a near-worst-case test of uniform-region availability. Both NPS and TTF pipelines were validated against phantom-derived references, confirming that in vivo measurements reflect genuine anatomical texture and resolution rather than pipeline artifacts. By eliminating operator variability and phantom requirements, AnatomIQ enables practical continuous quality monitoring and evidence-based reconstruction optimization. The toolkit is open-source, with optional commercial licensing for academic and clinical use.
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Medical Physics

Quiet, rapid 3D multiparametric mapping using magnetization-prepared zero echo time MRI

Alireza Samadifardheris, Shishuai Wang, Ana Beatriz Solana et al. (+10 more)

Purpose: To introduce and evaluate MuPa-ZTE, a quiet, rapid 3D framework combining native and magnetization-prepared zero echo time (ZTE) acquisitions for multiparametric mapping. Methods: MuPa-ZTE combines steady-state native ZTE with transient-state magnetization-prepared ZTE. Two implementations were evaluated: T2T1-ZTE for apparent proton density, T1, and T2 mapping, and T1-ZTE for apparent proton density and T1 mapping. Both were assessed in an ISMRM/NIST system phantom and two healthy volunteers; T2T1-ZTE was also demonstrated in a patient with brain metastases. The 10-minute phantom and 4.5-minute in vivo acquisitions were retrospectively truncated to 3, 2, and 1 minute and reconstructed with and without deep learning-based denoising. Evaluations included phantom agreement, precision, short-term repeatability, apparent SNR, edge sharpness, and consistency with full-duration in vivo maps. Results: T1 estimates remained close to nominal phantom values across implementations, durations, and reconstructions. T2 accuracy was maintained down to 2 minutes over the brain-relevant range, with limited sensitivity to longer T2 values. Denoising generally reduced variability and improved short-term repeatability. In vivo, 1.1-mm isotropic whole-brain maps were obtained in 4.5 minutes; denoising increased apparent SNR while preserving edge sharpness and yielded promising image quality after retrospective truncation to 2 minutes. Conclusion: MuPa-ZTE enables quiet, isotropic 3D multiparametric mapping within 4.5 minutes, supporting robust T1 mapping and T2 mapping over a brain-relevant range, with promising acceleration toward 2 minutes using deep learning-based denoising.
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All authors Alireza Samadifardheris, Shishuai Wang, Ana Beatriz Solana, Jose de Arcos, Noemi Sgambelluri, Emil Langensee Ljungberg, Sagar Mandava, Mika Vogel, Steven C. R. Williams, Stefan Klein, Juan Antonio Hernandez-Tamames, Dirk H. J. Poot, Florian Wiesinger

Wednesday 30 September

Medical Physics · Cross-listed from cs.CV

Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers

Sebastian Endt, Marcus Wirth, Johannes Reinhold Schlund et al. (+1 more)

Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods either resolve fiber orientations without quantifying microstructure, or quantify microstructure while assuming a fixed number of compartments and a single fiber direction. Nonparametric approaches that recover both require tensor-valued diffusion encoding and computationally expensive Monte-Carlo inversion of an ill-posed inverse Laplace transform. We propose to reframe this problem as an object detection-like task, adopting the Detection Transformer (DETR) architecture to jointly predict mean diffusivity (MD), fractional anisotropy (FA), main fiber direction, and signal fraction for a variable number of compartments per voxel from standard multi-shell diffusion MRI with linear encoding. Hungarian matching during training resolves permutation invariance across compartments. We introduce mean Average Precision as a reproducible benchmark metric. Evaluated on synthetic test data with up to five compartments per voxel, our model achieves $R^2=0.95$ for MD, $R^2=0.88$ for FA, and a median angular error of 4.2{\deg}, with performance scaling naturally with compartmental signal fraction.
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All authors Sebastian Endt, Marcus Wirth, Johannes Reinhold Schlund, Marion Irene Menzel

Medical Physics

An Uncertainty-Guided Digital Twin Framework for Online Adaptive Proton Therapy in Head and Neck Cancer: A Feasibility Study

Yizhou Wu, Ryan J. Sanford, Huiqiao Xie et al. (+12 more)

Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present an uncertainty-guided digital twin (UGDT) framework that forecasts treatment-day anatomy before treatment and evaluate whether it generates online adaptive proton therapy (APT) plans of clinical quality. Approach: A library of 302 longitudinal deformations from 88 previously treated HN patients was transported onto each new patient's treatment planning CT (TPCT) using two-step multi-atlas deformable image registration (DIR) built on a pretrained CT foundation model, generating about 284 predicted CTs (pdCTs) with contours per patient. Dispersion of propagated clinical target volume (CTV) contours defined a patient-specific robust margin. In ten patients, the quality assurance CT (QACT) triggering a replan represented treatment-day anatomy, and the physician-approved replan was the baseline. The pdCT most similar to the QACT (pdCT-H) and one from the lowest quartile (pdCT-L) were planned to within about 5% of baseline plan quality, forward-calculated on the QACT, and reoptimized to generate online APT plans. Main results: pdCT plans scored within -0.7% (pdCT-H) and -1.0% (pdCT-L) of baseline. Forward calculation on QACT reduced high-dose CTV D98% to 88.3% and 85.5%. After online reoptimization, D98% recovered to 98.3 +/- 0.3% and 98.2 +/- 0.3%, versus 98.5 +/- 0.4% at baseline. Spinal cord and brainstem doses remained below tolerance, and plan quality scores were within -1.1% (p = 0.19) and -1.7% (p = 0.01) of baseline. Significance: UGDT generated online APT plans comparable in quality to physician-approved offline replans using anatomy forecast before treatment, enabling a transition from reactive offline replanning toward anticipatory online adaptation.
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All authors Yizhou Wu, Ryan J. Sanford, Huiqiao Xie, Jie Ding, Shupeng Chen, Tung-Ho Wu, Ping-Hsiu Wu, Justin Roper, Jun Zhou, Minglei Kang, Bill Stokes, Sibo Tian, David S. Yu, Xiaofeng Yang, Chih-Wei Chang

Tuesday 29 September

Medical Physics

Using a radiopharmaceutical production cyclotron for radiobiological research

Eva Kasanda, Jan Gruber, Pierluigi Casolaro et al. (+8 more)

The Laboratory for High Energy Physics and the Institute of Anatomy at the University of Bern are establishing a pre-clinical proton therapy research facility at the Bern University Hospital (Inselspital). The facility centers on the Bern Medical Cyclotron, an 18 MeV compact cyclotron designed for microampere-range beam currents for radioisotope production. The Bern Medical Cyclotron can be adapted as a highly accessible and customizable platform for pre-clinical proton therapy research, enabling advanced modalities such as ultra-high dose rate (FLASH) radiotherapy and spatially fractionated radiation therapy. Human keratinocytes (HaCaT) and mouse melanoma (B16-F10) cell lines were irradiated with 8.14(29) MeV protons at conventional dose rates and with 225 kV X-rays at doses from 0 to 8 Gy. Clonogenic survival assays were analyzed using the linear-quadratic model, and relative biological effectiveness values were derived by comparing the proton and X-ray doses required for 0.1 survival. Proton irradiation yielded steeper survival curves than X-rays, indicating higher cytotoxicity. B16-F10 cells were most sensitive, with a relative biological effectiveness of 1.34 at 0.1 survival, while HaCaT cells showed a relative biological effectiveness of 1.21. These results confirm enhanced proton effectiveness in cell killing and demonstrate that the Bern Medical Cyclotron provides a reliable platform for radiobiological studies. Establishing in vitro proton irradiation capability marks a key milestone toward developing a comprehensive pre-clinical radiobiology research facility in Bern. Ongoing upgrades will further refine dosimetry and proton delivery, paving the way for future FLASH and proton spatially fractionated radiation therapy studies.
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All authors Eva Kasanda, Jan Gruber, Pierluigi Casolaro, Lars Eggimann, Alexander Gottstein, Isidre Mateu, Paolo Pellicioli, Maria Vittoria Rossi, Paola Scampoli, Cristian Fernandez-Palomo, Saverio Braccini

Medical Physics · Cross-listed from cs.CV

You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution

Prathamesh Pradeep Khole, Shreya Handa, Utkarsh Gupta et al. (+1 more)

Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and pixelwise uncertainty, most often on pairs built by synthetically degrading high-field images. Prior work acknowledges that these models hallucinate and that the problem is ill posed, but to our knowledge no study measures how much information about the individual subject the real low-field scan actually contains. We measure it. Using paired 64 mT and 3T scans of the same subjects from three public datasets, and a measurement protocol validated on tests whose correct answer is known in advance, we find that, judged over the whole brain, real 64 mT scans carry structure specific to the individual only down to approximately 3 to 4 mm half-pitch in plane, and coarser still through plane. Standard synthetic degradations preserve subject information roughly 1 mm beyond this ceiling, so models trained and benchmarked on synthetic pairs are evaluated on information that real scanners never record. We then test trained diffusion models and a publicly released external model on real paired acquisitions; 24 trained runs of five architectures (GAN, diffusion, and transformer families) give the coverage of the audit. On every subject where faithfulness can be measured, fine output detail is no more correlated with the subject's own 3T scan than with a stranger's, while sample-variance uncertainty does not distinguish fabricated structure from reconstruction difficulty. Because PSNR and SSIM score resemblance to a reference rather than whether detail belongs to the subject, a benchmark scored by them cannot tell recovery from fabrication. Code for the measurement protocol will be released so that recoverability claims can be tested for newer models.
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All authors Prathamesh Pradeep Khole, Shreya Handa, Utkarsh Gupta, Razvan Marinescu

Medical Physics

Quantifying the Impact of Upright Patient Positioning on Cardiac Substructures Using Deep Learning

Nicholas Summerfield, Yuhao Yan, Chase Ruff et al. (+4 more)

Upright patient positioners with diagnostic-quality vertical CT at treatment isocenter may improve image-guided radiation therapy (RT). However, cardiac substructure (CS) geometry in upright patients remains insufficiently characterized. This work evaluated if a supine-trained deep-learning (DL) CS segmentation model generalizes to upright CT images and quantified posture-dependent CS changes in thoracic patients, to assess potential CS-sparing benefits. 8 thoracic proton therapy patients underwent paired supine/upright 4DCT imaging. 20 CS and lungs were manually labeled on both datasets for positional comparisons. A previously developed supine-trained, DL pipeline generated the same CS, and performance was evaluated using Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). Upright CTs were rigidly registered to corresponding supine CTs by aligning the thoracic vertebrae, and CS centroid shifts were measured in the registered coordinate frame and relative to the carina. Paired differences were assessed using Wilcoxon signed-rank tests (p<0.05). The DL model successfully predicted all 20 CS on both upright (DSC, 0.65(0.24); HD95, 7.6(5.6)mm) and supine (DSC, 0.72(0.19); HD95, 5.8(2.6)mm) images yet with lower (p<0.05) performance upright. Upright positioning significantly increased median lung volume by 20.6% (range, -8.9%-42.8%). After vertebral alignment, most CS centroids shifted significantly inferior (median heart shift, 23mm; range, 18-37mm) and anterior (median heart shift, 5.0mm; range, 1.0-13.0mm) when upright. Relative to the carina, most CS shifted significantly inferior and closer anterior-posterior. A supine-trained DL model generalized to upright CT images for CS segmentation. Upright positioning produced increased lung volumes and significant inferior CS displacement, suggesting favorable geometry changes that may support cardiac-sparing workflows.
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All authors Nicholas Summerfield, Yuhao Yan, Chase Ruff, Mark Pankuch, Shae Gans, Niek Schreuder, Carri K Glide-Hurst

Monday 28 September

Medical Physics

Cardiac segmentation for ventricular tachycardia radioablation and radiotherapy patients with cardiac implants

Nicholas Summerfield, Dustin Jacqmin, Chase Ruff et al. (+8 more)

Deep-learning (DL) segmentation enables cardiac substructure (CS) delineation for advanced cardiac-sparing radiation therapy (RT). Patients presenting for RT may have implantable devices (cardioverter-defibrillators (ICDs)) and metal artifacts. Stereotactic body RT (SBRT) is used to treat ventricular tachycardia (VT), a population frequently with ICDs. We apply a DL model for RT patients with ICDs or undergoing VT-SBRT, enabling segmentation and CS-level dose analysis. CT simulation (CT-SIM) of 19 patients with ICDs and received VT-SBRT were evaluated. A validated segmentation model was retrained using a highly-curated CT-SIM cohort, supplemented with ICD images for training and validation, with a hold-out ICD test set (n=10), to predict 20 CS. Additional CT-SIM datasets included cancer-RT patients with (n=9) and without (n=10) ICDs to evaluate generalizability. The retrained ICD-supplemented model was compared against a non-ICD model using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and Wilcoxon signed-rank test (p<0.05). Dosimetric evaluation was performed for VT-SBRT CS. For VT-SBRT, the ICD-supplemented model achieved average DSC/HD95 of 0.71(0.19)/10.7(22.5)mm respectively, outperforming (p<0.05) the non-ICD model. On CT-SIM with ICDs, average DSC/HD95 were 0.70(0.22)/7.9(5.1)mm respectively, outperforming (p<0.05) the non-ICD model. On CT-SIM without ICDs, the average DSC was 0.74(0.17) (Non-ICD, DSC, 0.75(0.16), p<0.05) and average HD95 was 5.5(3.1)mm (Non-ICD, HD95, 5.3(2.8mm)). For VT-SBRT, CS dose depended on target location, with higher relative dose delivered to the ventricles, tricuspid valve, atrioventricular node, and left coronary arteries than other CS. ICD-supplemented training improved CS segmentation despite metal artifacts without meaningfully degrading non-ICD performance, supporting segmentation and CS-level dosimetry in ICD populations.
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All authors Nicholas Summerfield, Dustin Jacqmin, Chase Ruff, Andrew M. Baschnagel, Adam Burr, Michael Bassetti, Ryan Kipp, Matthew Kalscheur, Patrick M. Hill, Ming Dong, Carri Glide-Hurst

Medical Physics

Bridging cone-beam CT and MRI to stopping power ratio maps: a modality-agnostic Brownian bridge approach to support robust adaptive proton therapy

Yuhao Yan, Qisheng He, Minglei Kang et al. (+4 more)

Purpose: Adaptive proton therapy (APT) relies on routine offline CT simulations for updated anatomy. We present a modality-agnostic regularized Brownian bridge (rBBrg) framework to deterministically predict stopping power ratio (SPR) maps from CBCTs or MRIs. Methods: rBBrg consisted of (1) generation mapping where a Brownian bridge predicted SPR from input and (2) reconstruction mapping where a cGAN reconstructed input from predicted SPR. Class embedding was adopted for modality conditioning. Efficient, deterministic prediction was achieved via one-step sampling. Unimodality rBBrg and ResNet were implemented for comparison. Matched-pair planning CT-CBCT (n=37) and CT-MRI (n=21) from head-and-neck cancer patients were evaluated. A calibration phantom was scanned to establish Hounsfield look-up table for real SPR. Predicted SPR maps were evaluated via mean absolute error (MAE), peak-signal-to-noise ratio and structural similarity against ground truth with demonstration of dosimetric performance. Results: For CBCT-SPR synthesis, most metrics were comparable across models (p>=0.05). Modality-agnostic rBBrg outperformed ResNet in MAEbone (0.059 vs 0.067, difference=11%, p<0.05) and showed lower MAEexternal than ResNet (0.038 vs 0.040, p=0.05). Modality-agnostic and unimodality rBBrg were comparable (p>0.05). Qualitatively, both rBBrg better preserved anatomical fidelity than ResNet. For MRI-SPR synthesis, performance was overall comparable across models (p>0.05). Modality-agnostic rBBrg obtained MAEexternal=0.057. Dose calculation comparisons between real and CBCT-predicted SPR were higher in modality-agnostic rBBrg predictions compared to ResNet with gamma pass rate=98.8%/99.3% vs 94.5%/96.9% at 2 mm/2%, respectively. Conclusion: A novel modality-agnostic rBBrg model was developed to generate high fidelity, deterministic SPR from CBCT or MRI to support efficient and flexible APT workflow.
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All authors Yuhao Yan, Qisheng He, Minglei Kang, Behzad Hejrati, Christian Hyde, Ming Dong, Carri Glide-Hurst

Medical Physics

High-spatial resolution, high-quality white matter tractography using DTI with dynamic slice-by-slice B0 shimming in a head-only high-gradient performance 3T MRI scanner

Jerome J. Maller, Patricia Lan, Sherry Huang et al. (+4 more)

Data from conventional diffusion tensor imaging (DTI) using single-shot echo planar imaging (SS-EPI) acquisition are substantially influenced by magnetic field inhomogeneities (delta B0), which result in image distortion and signal dephasing. Multi-shot EPI can mitigate the delta B0-induced artifacts but at the cost of increased scan time. Recent brain tissue-selective, dynamic slice-by-slice B0 shimming techniques have demonstrated effective reduction of local delta B0 in the brain. When combined with DTI utilizing an echo-planar imaging (EPI) read-out, image distortion and signal dephasing are significantly reduced. The purpose of our study was to assess the impact of DTI with dynamic slice-by-slice B0 shimming technique (DySiBo) on white matter (WM) tractography. We retrospectively analyzed DTI datasets using SS-EPI and Multi-shot EPI with 2 shots (MS-2shot-EPI) readout at two spatial resolutions (1x1x2mm3 and 2x2x2mm3) with and without DySiBo. WM tractography was generated and inspected to examine the impact upon regions typically affected by delta B0-induced artefacts. We found more WM streamlines were generated in the DTI datasets with DySiBo in regions conventionally impacted by delta B0-induced in EPI images, including the brainstem, temporal and frontal lobes. DySiBo substantially improved the generation of WM streamlines in DTI-based tractography at both in-plane resolutions. In conclusion, DySiBo in conjunction with DTI requiring 10 diffusion tensor directions is sufficient to generate data with high enough SNR and angular resolution to resolve crossing fibers and produce high quality WM tractography in brain regions typically affected by susceptibility-induced artefacts. This has implications for quantitative WM microstructural indices and clinical evaluation of WM tracts in patients.
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All authors Jerome J. Maller, Patricia Lan, Sherry Huang, Chitresh Bhushan, Xinzeng Wang, Ante Zhu, Vincent A. Magnotta

Medical Physics

Physics-informed self-supervised generation of digital brain MRI phantoms from weighted images using differentiable MRI simulation

Kseniya Belousova, Anna Konanykhina, Zilya Murzina et al. (+2 more)

Purpose: To develop a physics-informed, self-supervised framework for generating digital brain MRI phantoms directly from conventional weighted MR images without requiring ground-truth parametric maps or anatomical segmentation. Methods: The framework predicts T1, T2, and proton density (PD) maps from T1-, T2-, and PD-weighted images and reconstructs the input images through an MRI signal model. Three generative architectures (variational autoencoder (VAE), generative adversarial network (GAN), and flow-based model) were compared. Models were pretrained on 3,739 synthetic brain slices generated using digital phantoms and an analytical MRI signal model, followed by fine-tuning on 90 real brain slices from three healthy volunteers. The best-performing architecture was subsequently fine-tuned using the differentiable MR-Zero numerical MRI simulator and evaluated on 30 held-out real slices. Results: The flow-based model demonstrated the highest overall performance and preserved fine anatomical details better than the VAE and GAN. After analytical-model fine-tuning, it achieved MS-SSIM values of 0.955-0.985 and PSNR values of 26.68-32.63 dB across T1-, T2-, and PD-weighted images. Fine-tuning with MR-Zero increased T1-weighted reconstruction quality from 0.955 to 0.977 (MS-SSIM) and from 26.68 to 30.60 dB (PSNR), and provided high robustness of metrics across different MR image weightings. The resulting digital phantoms also enabled simulation of images using previously unseen acquisition protocols. Conclusion: The proposed framework enables physics-informed generation of reusable digital brain MRI phantoms from weighted images using limited real-world data. Combining synthetic pretraining with differentiable numerical MRI simulation provides a practical approach for physically grounded MRI data augmentation without requiring reference parametric maps.
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All authors Kseniya Belousova, Anna Konanykhina, Zilya Murzina, Walid Al-Haidri, Ekaterina Brui

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

Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound

Camilla Ferrario, Maelys Venet, Olivier Villemain et al. (+1 more)

Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elastography (SWE) enables non-invasive tracking of myocardial stiffness dynamics over the cardiac cycle, providing a target for personalisation. However, mapping these observations to subject specific model parameters remains ill-posed, as multiple parameter sets can reproduce the same stiffness dynamics. We formulate SWE-informed personalisation as a statistical inference problem using simulation-based inference (SBI). Using a subject-adapted 0D cardiovascular model and neural posterior estimation, we estimate model-conditional posterior distributions over active stiffness scale k0, contraction rate kATP, and relaxation rate kSR, conditioned on SWE-derived curve features and subject specific context. Among six healthy volunteers, four passed objective prior-support diagnostics and were retained for quantitative posterior analysis. Curve-level RMSE against the observed SWE target decreased from 12.61 $\pm$ 5.55 kPa for the prior predictive median to 1.14 $\pm$ 0.38 kPa for the posterior predictive median, an 89.7 $\pm$ 4.2% reduction. Posterior analysis revealed parameter-specific uncertainty, k0-kATP compensation, weaker constraint of kSR, and the importance of prior-predictive diagnostics for assessing whether each subject is represented within the modelled SWE feature space. These results support SBI for uncertainty aware SWE-based personalisation, while identifying prior support and forward-model adequacy as key diagnostics.
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All authors Camilla Ferrario, Maelys Venet, Olivier Villemain, Maxime Sermesant

Medical Physics · Cross-listed from physics.optics

Contact lens with stretchable distributed-feedback laser for intraocular pressure monitoring

S.A. Ivanov, I.M. Fradkin, E.S. Musikhina et al. (+8 more)

Continuous monitoring of intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma, yet existing clinical methods rely on intermittent in-clinic measurements that miss critical diurnal fluctuations. Smart contact lenses are an attractive platform for continuous IOP tracking, and optical strain sensors are particularly promising thanks to their high sensitivity and natural readout. Most such optical sensors infer strain from the period of a regular structure - a grating or photonic crystal - whose deformation is read out optically. However, the precision of all sensors based on the period of regular structures is fundamentally bounded by the uncertainty principle, which severely limits performance at the millimeter length scales available inside a contact lens. Here, we address this limitation by integrating a distributed-feedback (DFB) laser, based on a surface-modulated ultrathin F8BT dye layer, into a soft polydimethylsiloxane (PDMS) contact lens. Operating at a symmetry-protected bound state in the continuum at the {\Gamma} point, the device produces a narrow lasing line whose wavelength shifts directly with grating strain - a quantity not constrained by the spatial uncertainty principle. The DFB structure is fabricated by UV holographic lithography and transferred onto the lens by a simple float-off process, yielding a stretchable, transparent, polymer-compatible sensor that is intrinsically scalable. Tested on a custom artificial eye model whose pressure-induced deformation is comparable in scale to that reported for the human eye, the sensor achieves a sensitivity of 0.027 nm/mmHg and a calibration residual of 1.2 mmHg under phantom conditions, a level relevant to tonometric monitoring, with substantial headroom currently limited mainly by auxiliary readout equipment.
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All authors S.A. Ivanov, I.M. Fradkin, E.S. Musikhina, R.V. Kirtaev, A.A. Khrebtov, A.A. Vyshnevyy, A.A. Marchenko, V.R. Solovei, I.P. Radko, A.V. Arsenin, V.S. Volkov

Sunday 27 September

Medical Physics

DAFNet: Dual-path Adaptive Fusion Network for High-Fidelity Cross-Modality Brain MR Image Synthesis

Jialin Liu and Xiaoliang Zhang

Cross-modality MRI synthesis using deep learning can streamline clinical workflows, compensate for missing image contrasts, and reduce examination time; however, existing methods often require substantial computational resources, limiting their practical accessibility. To address this limitation, we propose and investigate a lightweight yet effective framework, termed the Dual-path Adaptive Fusion Network (DAFNet), for T1-to-T2 images synthesis. DAFNet is based on cGAN (conditional generative adversarial networks) structure, and the generator employs a dual-path encoder that adaptively combines depth-wise separable convolutions and dilated convolutions to capture both fine-grained details and broader contextual features. A novel Dual-path Adaptive Fusion Module (DAFM) is introduced to dynamically combine these feature streams through channel-wise complementary weighting, enabling efficient and adaptive feature integration. This fusion mechanism is further applied to the final skip connection to enhance reconstruction fidelity, while the decoder adopts a standard transposed convolution architecture. Coupled with a conventional conditional GAN discriminator, the proposed model maintains low computational complexity while improving perceptual image fidelity. Experimental results demonstrate that DAFNet achieves superior performance compared with baseline U-Net and conventional cGAN models, reaching a peak signal-to-noise ratio (PSNR) of 26.43 dB while significantly reducing model size. This result indicates that DAFNet provides an effective balance between synthesis fidelity and computational efficiency, making it a promising solution for deployment on standard computing hardware and for broader clinical and research applications in MRI contrast synthesis.
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Friday 25 September

Medical Physics

Numerical Simulation of Electrical Properties in Cortical and Trabecular Bone: A Simplified Model

María José Cervantes, Catalina A. Cely-Ortíz, C. Manuel Carlevaro et al. (+1 more)

The electrical properties of biological tissues depend on their composition and microstructure and determine their response to applied electric fields. In bone tissue, these properties are closely related to its composition, porosity, and microstructural organization. However, simplified models that link bone microstructure (trabecular and cortical) to its effective electrical properties while accounting for the electrical anisotropy of cortical bone remain scarce. In this work, a multiscale approach was developed, combining finite element simulations with Bruggeman-based homogenization models. Trabecular bone was modeled using microCT derived geometries, and its effective conductivity was analyzed as a function of BV/TV and free water content. Cortical bone was represented by a simplified three-dimensional network of Haversian canals (axial = z) and Volkmann canals (transverse), incorporating their directional organization through an anisotropic Bruggeman formulation. The models captured the conductivity range reported experimentally for trabecular bone (approximately 25-200 mS/m) and reproduced the anisotropic behavior observed in the cortical FEM simulations, with higher conductivity in the axial direction. In the generated cortical geometries, 65.5% of the total porosity was associated with axial Haversian canals. At the macroscale, the tibia model with lower trabecular and cortical BV/TV and reduced cortical thickness showed an approximately 38-39% reduction in impedance relative to the reference model. These results indicate that microstructural variations in bone tissue are reflected in its effective electrical properties and in the macroscopic impedance of the model, providing a physical and computational basis for future investigations into the sensitivity of electrical measurements to changes in bone microstructure.
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All authors María José Cervantes, Catalina A. Cely-Ortíz, C. Manuel Carlevaro, Ramiro M. Irastorza

Medical Physics

Improving Multi-Delay-ASL through specialized reconstruction

Ingmar Sorgenfrei, Qinyang Shou, Ingrid Barth et al. (+3 more)

Purpose: Although image reconstruction has received relatively little attention in ASL research to date, it has the potential to address several challenges in ASL. As well as speeding up measurements by increasing undersampling and reducing measurement artifacts, it can improve the signal-to-noise ratio (SNR) of a given data set and the reproducibility of examinations. Methods: This work focuses on extending a dedicated ASL reconstruction approach (ASL-TGV) (Spann et al. (2020)) to multi-delay data and applying it to a high-resolution pCASL test-retest dataset. To show not only improvement in the Perfusion Weighted Images (PWIs), Cerebral Blood Flow and Arterial Transit Time was estimated and the test-retest reliability was estimated using the within subject Coefficient of Variance (wsCV), the Intraclass Correlation Coefficient (ICC) and Root-Mean-Squared-Error (RMSE). Results: The Perfusion-Weighted-Images reconstructed from highly undersampled single-shot data using ASL-TGV are clearly improved compared to a fully-sampled reference from the same data even after denoising, especially for long PLDs and the outermost slices. SNR calculated in grey and white matter ROIs shows an improvement of 62% and 35% respectively. The CBF maps produced from the ASL-TGV images have an improved test-retest reliability. Conclusion: ASL-TGV, which is now implemented in the Berkeley Advanced Reconstruction Toolbox (BART), can be used with any type of ASL labeling or data acquisition and any existing image post-processing pipeline and can improve SNR for the PWIs and reproducibility of the CBF maps.
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All authors Ingmar Sorgenfrei, Qinyang Shou, Ingrid Barth, Martin Uecker, Danny JJ Wang, Rudolf Stollberger

Medical Physics

A 2D autocorrelation-based frequency estimator reflecting spatial tissue distribution to improve Ultrasound H-scan tissue characterization

Jihye Baek, Thurston Brevett, Dongwoon Hyun et al. (+3 more)

H-scan is a promising quantitative ultrasound technique that estimates the frequency content of backscattered signals and maps the estimated frequencies onto a red/blue color scale to reflect underlying tissue properties. Although it relies on matched filters tuned to different frequencies, the broad spectral bandwidth of ultrasound produces noisy, granular displays. Here, we introduce an adaptive frequency estimator designed to suppress the noise within homogeneous regions while preserving sharpness across tissue boundaries. The method combines 2D autocorrelation with a matched filter. In the first stage, a matched-filter-based estimation yields an a priori map of the spatial distribution of frequencies. The local heterogeneity of these estimates then defines a 2D weighting function that guides a second estimation stage. Drawing on the concept of Loupas's blood velocity estimator, we apply autocorrelation over a 2D spatial kernel to recover the axial frequency components, employing a weighted summation that accounts for the spatial frequency distribution within the kernel. We benchmarked the proposed estimator against conventional approaches, including the short-time Fourier transform, the H-scan matched filter, and standard autocorrelation, using both Field II simulations and in vivo data from human subjects with hepatic steatosis. In simulation, our adaptive estimator reduced the noisy texture in homogeneous regions while retaining clear boundary delineation, whereas the other estimators could achieve only one of these objectives. Applied to the in vivo human liver, the estimator improved H-scan image quality by lowering noise and enhancing the discrimination of steatotic liver from adjacent gallbladder and skin layers.
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All authors Jihye Baek, Thurston Brevett, Dongwoon Hyun, Ahmed El Kaffas, Kevin J. Parker, Jeremy J. Dahl

Thursday 24 September

Medical Physics

MBFormer: Microbubble Transformer for 3D Time-Series Data Processing to Improve Bound Bubble Detection in Nonde-structive Ultrasound Molecular Imaging

Jihye Baek, Jeong Hoon Lee, Hoda Hashemi et al. (+4 more)

Development of nondestructive ultrasound molecular imaging (UMI) is essential for early cancer detection through real-time screening using clinical ultrasound systems. Current techniques face challenges in accurately detecting targeted microbubbles (MBs) bound to specific biomarkers, primarily due to false-positive detections of unbound free-floating MBs. We propose a transformer model for time-series video processing to improve the differentiation of bound MBs. We propose a hierarchical transformer, termed MBFormer (microbubble transformer), featuring a positional-embedding-free encoder and a lightweight decoder. Leveraging attention within 3D spatio-temporal data to effectively capture stationary signals from bound MBs while suppressing nonstationary signals from unbound MBs. Since MBs appears as relatively small textures compared with conventional segmentation targets in medical imaging, such as organs and tumors, we optimized the model with two hierarchical layers, each with an attention block, to process ultrasound video data. The network outputs the molecular signal amplitude to visualize fine MB textures. Performance was evaluated using an in vivo breast cancer model, compared against a prior CNN-based UMI method and SegFormer3D baseline, a representative 3D transformer. MBFormer (AUC = 0.943) outperformed both CNN (AUC = 0.897) and SegFormer3D (AUC = 0.766) in detecting bound MBs. The CNN showed residual molecular signal from free MBs in the cardiac chambers, whereas SegFormer3D failed to detect fine MB textures. Overall, MBFormer demonstrated enhanced detection of bound MBs while suppressing free MBs and achieved a frame rate of 16.7 to 18.1 FPS, demonstrating its potential for real-time application. We anticipate that this transformer-based UMI model can facilitate real-time, free-hand nondestructive UMI in clinical systems.
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All authors Jihye Baek, Jeong Hoon Lee, Hoda Hashemi, Arutselvan Natarajan, Farbod Tabesh, Ramasamy Paulmurugan, Jeremy J. Dahl

Medical Physics · Cross-listed from eess.IV

PGDM-MRSRGAN: Physics-Guided Degradation Model with an SRGAN Framework for Magnetic Resonance Image Super-Resolution: Applications in Low-Field MRI

Yashwant Kurmi, Charlotte R. Sappo, Sai Abitha Srinivas et al. (+3 more)

Magnetic Resonance Imaging (MRI) often suffers from low signal-to-noise ratio (SNR) and limited spatial resolution, which compromise clinical precision. This study aims to address these challenges by developing a physics-guided degradation model (PGDM) and a novel deep learning framework, Magnetic Resonance Super-Resolution GAN Imaging (MRSRGAN), for improved super-resolution reconstruction of MRI images. The proposed approach consists of two stages: (1) a physics-guided degradation model that simulates low-field MRI conditions by incorporating artifacts such as blur, B0 inhomogeneity, chemical shift effects, down-sampling, and noise to generate paired low-resolution (LR) and high-resolution (HR) training datasets; and (2) an MRSRGAN-based reconstruction network utilizing a hybrid loss function, including modified spectral angle mapper (MSAM) and sharpened ground truth images based on de-convolution of slice profile-based kernels to restore high-SNR and high-resolution images. Experimental validation demonstrates that the proposed method achieves superior spatial resolution, enhanced SNR, improved peak signal-to-noise ratio (PSNR), higher structural similarity index (SSIM), reduced MSAM error, and better non-reference image quality evaluation (NIQE) metrics. Furthermore, it effectively reduces artifacts and demonstrates robustness on real MRI datasets. The MRSRGAN framework, guided by the physics-informed degradation model, provides a significant improvement in MRI image quality, enhancing spatial resolution and diagnostic accuracy. Its demonstrated robustness and effectiveness on real MRI datasets highlight its potential to resolution enhancement for precise diagnostic imaging.
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All authors Yashwant Kurmi, Charlotte R. Sappo, Sai Abitha Srinivas, Selin D. Kandemir, Malvika Viswanathan, Zhongliang Zu

Medical Physics

Cone-beam artifact reduction in Gamma Knife CBCT images using a line-arc-line scan trajectory

Alexandra Alain-Beaudoin, Håkan Nordström, Luc Beaulieu et al. (+1 more)

Objective. Gamma Knife cone-beam computed tomography (CBCT) images suffer from distinct cone-beam artifacts for some patients, due to the conical X-ray beam which is oriented to intersect the detector perpendicularly at its inferior edge. The use of an exotic scan trajectory which provides more complete sampling across the field of view than the current arc can reduce cone-beam artifacts. In this study, the feasibility of a line-arc-line scan trajectory for the Gamma Knife CBCT is assessed through a proof of concept. Approach. Using a customized research Gamma Knife, a Catphan 503 and an anthropomorphic head phantom are imaged for different exotic scan trajectories, consisting of combinations of arcs and lines. The CBCT images for each trajectory are then reconstructed with an iterative algorithm. Main results. A line-arc-line trajectory provides the largest image quality improvement among the investigated trajectories, with no noticeable cone-beam artifacts in the CBCT images: the axial interfaces between modules of the Catphan are well defined, and the superior edge of the phantom on the axial axis is sharper by 92%, compared with the current arc trajectory. For the head CBCT images, the proposed trajectory removes most of the cone-beam artifact on the superior side of the skull, characteristic of the current Gamma Knife CBCT images. The artifact reduction also leads to a visual improvement in terms of uniformity in both phantoms. Significance. A line-arc-line CBCT scan trajectory would be feasible on the Gamma Knife with limited changes to the current configuration, and could produce images with improved image quality.
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All authors Alexandra Alain-Beaudoin, Håkan Nordström, Luc Beaulieu, Joakim da Silva

2609.30169 · 24 Sep · This paper is 13 pages in length, including 7 figures and 2 tables, and presents the continuation of the study presented in the Preprint arXiv:2609.28232v1 [physics.med-ph]. The contents of this paper were presented at the 2026 Joint AAPM | COMP Annual Meeting. The manuscript has been submitted to Physics in Medecine & Biology

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Medical Physics

Melanin- and linewidth-corrected pulse-oximetry

Giles Blaney, Jodee Frias, Ravi Durbha et al. (+2 more)

Current pulse oximeters tend to overestimate arterial saturation in darkly pigmented patients, increasing occult hypoxemia rates. One proposed mechanism is spectral coloring: melanin contributes to reshaping the detected spectrum of a broad-linewidth source, making the wavelength-dependent parameters behind calibration skin-tone-dependent. Diffuse optics theory allows for a melanin- and linewidth-corrected calibration equation, which to our knowledge has never been tested in vivo. In this work, we fit the empirical linear equation, the diffuse optics theoretical equation, and its corrected form to the public OpenOximetry Repository—considering 98 patients with paired arterial blood-draws, raw photoplethysmograms, and skin reflectance spectra. Accuracy is practically indistinguishable between the three approaches, yet the saturation error's dependence on melanin falls from +2.8 to +1.2 to +0.1 percentage points per unit melanin volume fraction—consistent with the spectral-coloring theory. None of these dependencies on melanin are statistically resolved here—a cohort roughly ten times larger is needed—so the correction is backed by theory and its expected direction. All three calibration approaches carry two degrees of freedom: the theoretical equation drops into existing calibration procedures, and its melanin-aware form needs a per-patient skin-tone measurement. This work demonstrates how the theoretical pulse oximetry model can be applied in calibration.
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All authors Giles Blaney, Jodee Frias, Ravi Durbha, Sergio Fantini, Valencia Koomson

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

Mathematical Modelling of Within-Host HIV Dynamics with Cytotoxic Immune Response and Antiretroviral Therapy

Maxim Polyakov and Yulia Chernikova

We develop and analyse a mechanistic ODE model of within-host HIV dynamics that includes uninfected CD4$^+$ T cells, latently and productively infected cells, free virions, and cytotoxic immune effectors. Antiretroviral therapy is described by two time-dependent efficacy functions that separately reduce new infections and virion production. We show that solutions remain non-negative and bounded, identify the infection-free and endemic equilibria, and derive the basic reproduction number together with the local stability condition for the infection-free state. We also obtain treatment-dependent suppression thresholds and an analytical estimate of when this threshold is crossed as treatment efficacy declines. Numerical simulations consider untreated infection, ART initiation, periodic variation in efficacy, and progressive loss of treatment effect. Without therapy, the model approaches a state of persistent infection. ART reduces viral load and promotes recovery of the CD4$^+$ T-cell population, while the latent reservoir persists under the parameter set considered. Periodic changes in treatment efficacy produce sustained forced oscillations. These results connect the analytical threshold conditions with the treatment-dependent dynamics of the model and provide a basis for future calibration, sensitivity analysis, uncertainty quantification, and extensions informed by clinical data.
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Wednesday 23 September

Medical Physics

GenMC: Real-Time Generative Monte Carlo Surrogate for Quantitative Photoacoustic Imaging

Mengjie Shi, Feng He, Tom Vercauteren et al. (+1 more)

Photoacoustic (PA) imaging provides molecular and functional information about tissue, such as blood oxygen saturation (sO2), yet its clinical translation is hindered by inaccurate quantification. A major source of error is the spectral colouring effect, in which wavelength-dependent optical attenuation distorts the local optical fluence. Monte Carlo (MC) simulation is the gold standard for modelling light transport, but its computational demand precludes real-time use. Here, GenMC is presented, a deep generative framework based on a conditional generative adversarial network that estimates optical fluence distributions from tissue anatomy and literature-derived optical properties, with anatomical priors obtained from co-registered ultrasound images. Trained on MC-generated synthetic datasets, GenMC produces high-fidelity fluence maps in under 30 ms per frame, a four-orders-of-magnitude speed-up over conventional MC simulation, and reaches peak signal-to-noise ratios of up to 36.24 dB in vivo, outperforming UNet and Pix2Pix baselines. Validation in blood-mimicking phantoms and in 37 human volunteers spanning Fitzpatrick skin types III-V demonstrates improved accuracy, robustness, and physiological consistency of sO2 estimation. By enabling real-time, accurate, and reproducible quantification of tissue oxygenation, GenMC addresses a critical barrier to quantitative PA imaging and offers a general strategy for rapid, high-fidelity approximation of light transport in tissue.
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All authors Mengjie Shi, Feng He, Tom Vercauteren, Wenfeng Xia

Medical Physics

Non-circular scan trajectories for reducing cone-beam artifacts in Gamma Knife CBCT images: a simulation study

Alexandra Alain-Beaudoin, Håkan Nordström, Luc Beaulieu et al. (+1 more)

Objective. Gamma Knife cone-beam computed tomography (CBCT) images are deteriorated by cone-beam artifacts whose magnitude increases along the superior direction. In this study, a novel scan trajectory compatible with the Gamma Knife CBCT system is optimized to reduce cone-beam artifacts, with the aim to replace the current 200-degree single-arc scan. Approach. Data sampling analysis with tomographic incompleteness maps indicates the level of undersampling across the field of view based on the geometry of the system and of a scan trajectory. Moreover, CBCT simulations are performed from a virtual phantom with disks aligned along the axial direction and from a CT reconstruction of a stereotactic end-to-end validation (STEEV) phantom. CBCT projections are simulated for a given scan trajectory through a polychromatic forward projection model with added noise and scatter, then the CBCT image is reconstructed using an iterative algorithm which minimizes weighted least squares. Main results. Both the incompleteness analysis and the CBCT simulations indicate adding lines to the current single-arc trajectory is more efficient to reduce cone-beam artifacts than adding more arcs, both in terms of number of additional projections and new artifacts. A line-arc-line trajectory with source axial steps of 3.5 mm removes virtually all cone-beam artifacts. The widths of the cone-beam artifacts created by the disks show a positive correlation between the artifact magnitude and the incompleteness value. Significance. A line-arc-line scan trajectory is promising to reduce cone-beam artifacts of the Gamma Knife CBCT images while being a compatible and reasonable trajectory for the current system design.
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All authors Alexandra Alain-Beaudoin, Håkan Nordström, Luc Beaulieu, Joakim da Silva

2609.28232 · 23 Sep · This paper is 18 pages in length, including 7 figures and 1 table. The content of this paper was presented at ESTRO2026, with the reference to the conference abstract as follows: Alain-Beaudoin, A. et al. (2026). Radiother. Oncol., 218, S2059-S2061. The manuscript has been submitted to Physics in Medecine & Biology

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Medical Physics

Integration of Spectral CT with PET and SPECT: Bringing Tissue Composition Information to Molecular Imaging

Guobao Wang, Kris Thielemans, Peter B. Noël et al. (+2 more)

Molecular imaging has been transformed by integrating positron emission tomography (PET) or single-photon emission computed tomography (SPECT) with x-ray computed tomography (CT). However, x-ray CT in hybrid imaging is used primarily for anatomical localization and for attenuation and scatter correction of emission data. Its ability to characterize tissue composition remains underutilized. Spectral CT, including dual-energy CT (DECT) and photon-counting CT (PCCT), can provide material-specific information, such as images of iodine concentration, bone or calcium fractions, electron-density, effective atomic number and other spatially-varying material properties. These capabilities create an opportunity to bring tissue-composition information into molecular imaging. In addition, spectral CT can also reduce CT artifacts and provide more accurate corrections for emission data. In this review, we discuss the technical basis, integration pathways, and translational opportunities for combining spectral CT with molecular imaging. We review sequential and integrated PET/DECT approaches, emerging PET/PCCT concepts, PET-enabled spectral CT, and extensions to SPECT/CT. We then examine how spectral CT information may improve attenuation correction, scatter correction, tissue-fraction correction, positron range correction, reconstruction priors, and dosimetry. Clinical and translational applications include contrast-enhanced molecular imaging, vascular and perfusion imaging, quantitative bone marrow imaging, musculoskeletal imaging, and theranostics. Overall, spectral CT integration may move hybrid molecular imaging toward tissue-composition-informed molecular imaging, in which radiotracer signals are interpreted within their material context.
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All authors Guobao Wang, Kris Thielemans, Peter B. Noël, Joel S. Karp, Paul E. Kinahan

Medical Physics

Explicit pixel-value correction of lateral response artifacts in radiochromic film dosimetry: Evaluation of single-, double-, and triple-channel methods

Lixiang Guo and Ken Kang-Hsin Wang

Background:Flatbed scanners introduce a lateral response artifact (LRA) perpendicular to lamp motion. Because LRA varies with position, pixel value (PV), color channel, and film model, it may violate the channel-common perturbation assumption of multichannel dosimetry. Purpose:To characterize PV-domain LRA in EBT4 and EBT-XD films, compare Lewis and Full corrections, and evaluate their effects on single-, double-, and triple-channel dosimetry. Methods:Seven films were scanned at seven lateral positions using a cyclic-shift design. For each position and RGB channel, center-equivalent response was modeled as PVc = A + B * PV. Lewis correction used two films with linear interpolation of A and B; Full correction used all center-local pairs with PCHIP interpolation of PVc - PV. Performance was assessed by portrait-landscape profile consistency and dose accuracy. Results: LRA was affine in PV, asymmetric about scanner center, and dependent on film model and channel. Both corrections reduced profile differences. Mean absolute profile differences across MU levels ranged from 1.4%-5.3% for EBT4 and 2.4%-6.8% for EBT-XD. The methods performed similarly for EBT4, while Full correction reduced discrepancies for several EBT-XD double- and triple-channel methods. Residual blue-channel errors limited blue, red-blue, and triple-channel performance. At 500 MU and above, red showed the best dose agreement; at 100 MU, both corrections increased deviations for several single-channel estimates. Full correction produced smaller deviations than Lewis in several EBT-XD conditions. Conclusions:LRA should be corrected in the raw-PV domain before dose reconstruction. Full correction better preserved profile consistency and dose accuracy, particularly for EBT-XD. Because improved profile consistency did not always improve central-dose agreement, both should be evaluated separately in commissioning.
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