Physics in Medicine & Biology

Background: Independent end-to-end verification of adaptive radiotherapy on MR-Linac systems is limited by the lack of patient-specific phantoms able to reproduce imaging and dosimetric properties from CT and MRI scanners. We present a method for automated generation of 4D, patient-specific, multi-material 3D-printable phantoms for quality assurance of adaptive radiotherapy on a 1.5T MR-Linac. Methods: Patient images were automatically segmented using a pretrained deep learning model. The segmented structures were converted into high-resolution 3D meshes and assembled into printable phantoms. A dosimeter holder was inserted at user-defined anatomical locations, with orientation optimized to avoid traversal across heterogeneous tissue interfaces. Physiological motion was incorporated by generating phantoms from images at different timepoints and interpolating deformation fields to create continuous 4D models. Multi-material organs designed by mixing a set of six polymers at various proportions were used to reproduce tissue-specific imaging properties. The properties of material mixtures were evaluated in a clinical CT simulator and a 1.5T MR-Linac. Results: The proposed workflow enables automated generation of anatomically realistic phantoms with several types of embedded dosimeters. A discrete search method was designed for placement and immobilization of OSLD, film, and ion chamber dosimeters. Calibration curves for Hounsfield units were derived through variations in radiopaque material content, while MR signal intensity was modulated by gel and tissue matrix mixtures. Patient-derived abdominal phantoms were fabricated at multiple scales while replicating internal anatomical detail. Multi-dimensional phantom generation enabled continuous representation of motion states with consistent mesh topology across phases. Conclusions: We demonstrate an end-to-end workflow for automated generation of 4D patient-specific phantoms for MR-Linac quality assurance. The method combines realistic anatomy, embedded dosimetry, multimodal imaging properties, and physiological motion within a single fabrication framework. This approachmay enable an improved validation of adaptive radiotherapy workflows in MR-guided treatment devices.

ABSTRACT Background: Metallic implants such as orthopedic screws, prostheses, and dental hardware produce beam-hardening, photon-starvation, and streak artifacts that degrade computed tomography (CT) image quality, and the metal artifact reduction (MAR) methods developed to mitigate them require objective, reproducible benchmarking. Purpose: Objective evaluation of MAR algorithms in CT is hindered by the absence of phantoms that simultaneously provide anatomically realistic backgrounds, embedded implants of known geometry, and controllable, ground-truth--referenced artifact intensity. We present a dual-filament, voxel-level three-dimensional (3D) printing method that fulfills these requirements and demonstrate its capabilities on a clinically representative cervical spine case with embedded orthopedic spinal screws. Methods: The proposed method extends the PixelPrint framework, a fused-deposition-modeling (FDM) workflow that converts clinical Digital Imaging and Communications in Medicine (DICOM) data directly into 3D-printer Geometric code (G-code) without intermediate segmentation or surface meshing, to interleaved, voxel-level deposition of two filaments: a calcium-doped polylactic acid (PLA) for soft tissue and bone, and a higher-attenuation metal-doped PLA for metallic implants. For demonstration, anonymized DICOM data of a healthy cervical spine were used to design and fabricate three matched phantoms, each with six embedded spinal screws at C4--C6: a 0% metal-infill ground-truth phantom, a 50% medium-metal-infill phantom, and an 85% high-metal-infill phantom. All phantoms were scanned on a clinical spectral CT system at 120 kVp and 1000 mAs, reconstructed at 0.67 mm slice thickness with virtual monoenergetic imaging (VMI) across 50--190 keV. Method performance was characterized by region of interest (ROI)-based Hounsfield Unit (HU) agreement with the source patient data and by the noise-independent Gumbel-distribution p-index metric. Results: The dual-filament method reproduced patient anatomy, soft-tissue contrast, and screw geometry with high fidelity. ROI HU values agreed with patient data within {+/-}25 HU for soft tissue and trabecular bone; cortical regions were underestimated owing to the current ceiling of the calcium-doped PLA used in this study. The tunable-artifact behavior was quantified as follows: the Gumbel location parameter scaled monotonically from 46.7 HU (no-metal background) to 57.1 HU (50% infill) to 90.5 HU (85% infill) for the VMI 70 keV with standard filter. High-keV VMI reconstructions substantially reduced streak and beam-hardening artifacts while preserving anatomic detail. Conclusions: The proposed dual-filament, voxel-level PixelPrint method enables the fabrication of patient-specific, multi-material CT phantoms with embedded metallic implants and controllable, ground-truth--referenced artifact intensity. Although demonstrated here in a single cervical-spine case, the workflow is anatomy- and implant-agnostic by construction and could in principle be adapted to other musculoskeletal sites (e.g., knee, hip, dental) and implant materials, providing a reproducible methodological foundation for benchmarking MAR algorithms, characterizing spectral CT performance, and validating emerging photon-counting detector systems. Keywords: 3D printing methodology; fused deposition modeling; voxel-level multi-material printing; spectral computed tomography; metal artifact reduction; phantom design; orthopedic implants; dual filament; PixelPrint.

Objective: Preclinical small-animal irradiators such as the FLASH-SARRP can support the advancement of photon-FLASH toward the clinic. This study aimed at characterizing the FLASH-SARRP and established a robust quality assurance (QA) workflow to enable accurate and reproducible preclinical experiments. Approach: Custom 3D-printed spacers were designed to ensure reproducible X-ray tube alignment, sample positioning and mounting of the dosimetric tools. Beam characteristics were evaluated using a combined dosimetric approach. High spatially resolved dose distributions were obtained from Gafchromic films, whereas a plastic scintillating fiber was employed to monitor in real-time the temporal pulse structure and synchronization between the two X-ray tubes. Day-to-day variability of the delivery was evaluated over several sessions. Main results: The FLASH-SARRP achieved dose-rates of around 80 Gy/s when both tubes were used simultaneously and provided a homogeneous irradiation field suitable for small-animal studies. A desynchronization between the two tubes was observed with an average delay of 10 ms, resulting in temporal dose-rate heterogeneity. Additionally, a substantial inter-session variability (~11%) was found, whereas the intra-session variability was relatively low (~4%). Inter-session variability was reduced to 5%, approaching the intra-session variability, by adding Gafchromic films/scintillator-based quality assurance (QA) workflow into the irradiation routine. Significance: This work highlights the importance of temporal dosimetry for preclinical FLASH studies. Additionally, a practical QA framework is proposed integrating real-time monitoring with reference dosimetry. The proposed work enables adaptive dose delivery, thereby enhancing the reproducibility of the irradiations, which is crucial for reliable preclinical studies on the FLASH effect.

Accurate segmentation of thin-wall organs-at-risk (OARs)-the cochlea, vestibular semicircular canals, internal auditory canal, tympanic cavity, and middle ear-is clinically relevant for head-and-neck radiotherapy planning, yet these small, thin-wall structures remain among the most challenging targets for automated delineation. Dual-frequency feature fusion is a promising direction for boundary-sensitive representation, but under the investigated FP16 FFT-FcaNet setting, we observe an approximately 863-fold activation-scale mismatch between the FFT and FcaNet branches, causing a nominal 5 percent residual coefficient to behave as an approximately 43-fold dominant term. We propose FreqFuseNet, which resolves this mismatch by normalizing the FcaNet branch to the FFT activation scale before residual injection with a fixed low-amplitude coefficient (beta = 0.05), restoring beta as an interpretable 5 percent residual-amplitude coefficient relative to the FFT feature scale. Under a controlled binary per-OAR ROI protocol on the SegRap2023 head-and-neck CT benchmark across 10 clinically prioritized thin-wall OARs, FreqFuseNet achieves Dice of 0.849, HD95 of 0.824 mm, and SDice@1mm of 0.959 in the primary seed, with comparable performance in an independent second seed (Dice 0.843, HD95 0.823 mm). FreqFuseNet yields statistically significant case-level aggregate improvements over 3D U-Net and MedNeXt-S (Wilcoxon p < 0.01 and p < 0.05, respectively), using only 29.7 million parameters versus 414.6 million for the full wavelet baseline.

Artificial intelligence (AI) applications in computed tomography (CT) imaging require objective and continuous testing, yet standardised methods for this purpose have not been established. Here, we present a framework using physical phantoms for standardised testing and monitoring of AI, demonstrated in liver lesion detection. We begin by designing phantoms tailored to the anatomical input domain expected by AI algorithms, and then systematically assess how AI performance is affected by variations in scanner technology and operation across two clinical CT systems. Next, we perform longitudinal monitoring, yielding consistent results over fifteen months on both systems. Finally, we validate clinical relevance by demonstrating that AI models trained on phantom data generalize effectively to patients and exhibit no evidence of phantom-specific adaptation. Our findings show that anatomically realistic phantoms enable standardised, site-specific testing and monitoring of AI, providing a proactive method for local and cross-institutional quality assurance.

Background: Three-dimensional tumor spheroids are an established radiobiology model, but scalable, reproducible readouts of dose-dependent radiation response are lacking. We evaluated whether optical coherence tomography (OCT) radiomics can quantify dose-associated response in spheroids, and how it compares with conventional brightfield morphology. Methods: This in vitro, cross-sectional study used SAS oral squamous cell carcinoma spheroids seeded at two densities (5000 and 10000 cells), irradiated at 0 to 12 Gy, and imaged on days 1 to 11 post-irradiation. Each OCT acquisition yielded co-registered structural-intensity and speckle-variance volumes. Radiomic features (shape, first-order, texture) were extracted with Radiomics.jl, filtered for repeatability, correlation-pruned, and ensemble-ranked. Dose correlation was assessed by repeated 5-fold cross-validation across five regressors, comparing brightfield-only (BF), OCT-only, and combined OCT+BF feature sets with paired Wilcoxon tests. Results: OCT-only models consistently outperformed the BF baseline (median R2 0.77 to 0.85 versus 0.61 to 0.69; p<0.001 for all regressors). Adding brightfield to OCT gave no consistent benefit, reaching significance only for Random Forest (p=0.026, power 0.62). A compact shared feature subset combined brightfield area dynamics with OCT texture, shape, and speckle-variance descriptors, all showing low repeat-scan variability relative to cohort variability. Conclusions: OCT radiomics provides a sensitive, reproducible, label-free high-throughput readout of spheroid radiation dose response that outperforms the current brightfield-based approach, without requiring concurrent brightfield acquisition.

Laser Interstitial Thermal Therapy (LITT) is a minimally invasive neurosurgical technique in which a stereotactically-implanted fiber delivers thermal energy to ablate intracranial lesions. Existing computer-assisted planning systems optimize trajectories against a one-dimensional line abstraction, then approximate the ablation zone as a fixed-radius cylinder post-hoc to estimate coverage. Trajectories selected as optimal under this model are not guaranteed to remain optimal once the cylindrical extent is applied, which introduces a mismatch between predicted and true ablation coverage. This may also underestimate spillover into surrounding healthy tissue. We present OptiLITT, a treatment planning system that represents the laser probe as a cylindrical ablation volume from the onset of optimization, jointly solving dual-fiber placement, lesion coverage, and healthy-tissue spillover as a single coupled problem. All planning parameters are exposed through a user-configurable graphical user interface supporting intraoperative refinement between planning stages.

PurposeTo develop and evaluate a multi-model consensus deep learning approach for automated gold fiducial marker (FM) segmentation in T1-weighted prostate MRI. Materials and MethodsIn this retrospective study, T1-weighted MRI and CT-derived reference standard segmentations were collected from 127 prostate cancer patients (all male; mean age, 70 years {+/-} 7 [standard deviation]; age range, 50-88 years; collected between October 2020 and January 2026) who each had three implanted gold FMs. A 3D U-Net was trained on 93 subjects using four random seeds to produce an ensemble. At inference, marker-class probability maps were averaged across models and the top three connected components selected. Performance was evaluated on 34 temporally held-out subjects (9 tuning, 25 test) using marker-level sensitivity and precision with exact (Clopper-Pearson) 95% confidence intervals (CIs). A model count ablation study was performed. The pipeline was deployed for on-scanner processing on Siemens MRI systems via the OpenRecon framework and as a browser-based application using WebAssembly, executing entirely client-side. ResultsThe four-model consensus achieved 96% (70 of 73) sensitivity and 95% (70 of 74) precision on 25 test subjects, with 29 of 34 (85%) subjects achieving perfect marker detection. Single models had a mean sensitivity of 84% (SD, 9%), improving to 96% with four-model consensus (SD, <1%). ConclusionMulti-model consensus deep learning substantially improved FM segmentation reliability over individual models, achieving high sensitivity and precision using only routinely acquired T1-weighted MRI.

Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety and clinical decision processes. Current workflows remain fragmented, difficult to audit and poorly standardised for evaluating domain-specific AI support. Methods: We developed RadGuide AI, a nuclear medicine agent built around a traceable data-model-tool loop. Patent, literature and clinical-trial records were converted into 15,596 initial QA items; relevance screening, completeness checks, semantic deduplication and cross-validation retained 5,474 core QA items. MedGemma-27B-Instruct served as the foundation model and was adapted with LoRA. The system incorporated 55 MCP-wrapped tools covering radiopharmaceutical R&D, clinical decision support, imaging analysis and radiation-safety/dosimetry. Evaluation used a locked N=200 benchmark with predefined denominators, leakage control, expert scoring, statistical procedures, factuality audits and tool-execution metrics. Results: RadGuide-LLM achieved 88.5% answer accuracy (177/200; 95% CI, 83.3-92.2%) and a Macro-Average score of 21.5/25 (bootstrap 95% CI, 20.9-22.0), exceeding GPT-4o, DeepSeek-V3.2 and the base MedGemma model in this technical evaluation. Supplementary audits reported guideline compliance, terminology recall, knowledge coverage, tool-routing success and preclinical/phantom dosimetry agreement with explicit denominators and confidence intervals. Interpretation: RadGuide AI converts nuclear medicine queries into auditable retrieval, tool selection, calculation, verification and reporting workflows. The findings support technical feasibility, not definitive patient-level clinical validation; prospective multicentre studies and external benchmark release remain required before clinical deployment.

Clinical treatment planning of near-infrared (NIR) brain stimulation requires patient-specific light dosimetry to optimize fluence delivery to cortical targets. The gold-standard Monte Carlo (MC) photon transport forward solver is accurate but computationally expensive and non-differentiable for personalized inverse design across subjects. Here, we present a foundation-model (FM)-encoded, differentiable implicit-neural surrogate for the MC solver. A pretrained 3D MRI/CT foundation model, VISTA3D, is domain-adapted to head phantoms with known optical properties to encode the subject anatomy. Next, an implicit neural representation is used to predict light fluence at arbitrary continuous coordinates. This formulation enables off-grid queries and gradients with respect to illumination parameters. Trained with a physics-informed, decade-stratified loss, the surrogate attains R2 {approx} 0.90 on held-out subjects. Ablation results show that the FM benefit is contingent on domain adaptation. Benchmarked against standard learned surrogates, our model is the most accurate in the high-dose region and best on dose-fidelity metrics ({gamma}-index, treated-volume DICE). Finally, gradient-based optimization through the surrogate recovers MC-consistent illumination configurations 50-240 x faster.

CT perfusion (CTP) is central to acute-stroke and oncologic imaging, yet quantitative outputs vary substantially across vendor software, undermining reproducibility. We present an open, transparent core (ctp-core) that fits first-pass time-attenuation curves with a gamma-variate model, derives perfusion indices (peak enhancement, time-to-peak, bolus-arrival time, and area under the curve) analytically from the fitted parameters, and renders parametric maps with the ASIST-Japan standardized lookup table (a-LUT) so that visualization is comparable across sites. Every parameter, bound, and processing step is exposed. The method is validated on Monte-Carlo synthetic curves with known ground truth; no confidential or patient data are used. Across signal-to-noise ratio (SNR) levels 5 to 100 (200 independent runs per level) the pipeline recovers peak time to within 0.03-0.52 s and peak amplitude to within 0.4-8.1% (mean absolute error), degrading monotonically with noise; at a representative SNR of 20 it recovers peak time within 0.13 s, peak amplitude within 2.0%, and bolus-arrival time within 0.51 s, with fit quality R-squared = 0.98. The reproducibility demonstration is deterministic (fixed seed) and re-runs to bit-stable metrics. All code, the synthetic-data generator, the standardized-visualization module, evaluation scripts, and a 34-test suite are released openly for independent verification. The contribution is a fully open, parameter-transparent gamma-variate plus standardized-visualization pipeline with reproducible synthetic benchmarks: a reference others can audit, reuse, and build on.

Viscoelastic characterization of tissue has significant diagnostic value in oncology, as tumor progression alters both elasticity and viscosity in ways that neither property alone can fully capture. Existing acoustic radiation force (ARF)-based methods such as Viscoelastic Response (VisR) ultrasound estimate relative elasticity and viscosity through per-A-line nonlinear model fitting, which is computationally intensive and requires auxiliary simulations to correct elasticity-dependent bias. This work presents VESTA (Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features), a two-stage data-driven pipeline that predicts elasticity ratio (ER) and viscosity ratio (VR) directly from seven normalized ARFI displacement features at the A-line level, without model fitting or compensation. Stage~1 is an MLP classifier that detects inclusion boundaries from normalized peak displacement and negative peak velocity ratios; Stage~2 is a dilated Conv1D regression model that estimates ER and VR along the full axial sequence using the predicted mask alongside displacement features. The pipeline was trained on 500 simulated inclusion scenarios spanning three geometries, five focal depths, two F-numbers, and a broad range of material contrasts. In silico, mean predicted ER and VR were within 12\% of ground truth across all geometries, with performance best when ER and VR were moderate or decoupled. Experimental validation on a chicken breast phantom demonstrated plausible generalization to real tissue heterogeneity. Applied to an in vivo murine 4T1 breast cancer model, the pipeline tracked treatment-related attenuation of mechanical contrast in paclitaxel-treated tumors relative to controls over a 36-day imaging period, supporting its relevance for tumor monitoring.

Objective: This study aims to develop and validate a quantitative, depth-resolved anisotropy imaging framework that extends ARFI-based focal degree-of-anisotropy (DoA) estimation into two-dimensional mapping by modeling the depth-dependent relationship between shear modulus ratio (SMR) and peak displacement ratio (PDR). Methods: We propose APRIL (Adaptive Polynomial Regression for anisotropy Imaging via ARFI-induced DispLacements), a framework for quantitative, depth-resolved DoA imaging that adaptively selects polynomial regression or shape-preserving spline interpolation based on excitation PSF asymmetry. Training data were generated using an LS-DYNA3D + Field II simulation pipeline in homogeneous transversely isotropic media (SMR 0.9-4.9). Testing included shifted SMRs under varied acoustic conditions and three heterogeneous inclusion configurations (anisotropic inclusion in isotropic background and vice versa). Experimental validation was performed in an in-vivo murine tumor model over the time, ex-vivo chicken breast, and tissue-mimicking gelatin phantoms, using a Verasonics system with an L11-5v transducer. Results: APRIL achieved depth-resolved SMR prediction errors below 9% over 10-30 mm, with highest accuracy in the focal region (MAE 2.3%, RMSE < 0.1) and stable performance across PSF transition zones. In heterogeneous phantoms, it reconstructed anisotropy maps with SSIM up to 86% and MPE below 7%, accurately delineating inclusion boundaries. Under acoustic parameter variations, mean absolute errors remained below 10%, demonstrating robustness to system and tissue heterogeneity. Conclusion: APRIL enables robust, two-dimensional anisotropy imaging beyond focal estimates. Significance: The method provides a physically grounded and generalizable framework for clinically viable anisotropy biomarkers in muscle, tendon, kidney, tumor and breast tissues.

We developed a standardized, reproducible preprocessing framework for computed tomography (CT) imaging data from multi-institutional repositories such The Cancer Imaging Archive (TCIA), enabling consistent radiomics and artificial intelligence (AI) analyses. Imaging data from TCGA-KIRC patients available on TCIA were used as a representative heterogeneous dataset characterized by variation in acquisition protocols, inconsistent metadata, and differing image quality. The proposed modular pipeline includes series filtering, DICOM-to-NIfTI conversion, orientation harmonization to a canonical coordinate system, voxel spacing normalization, intensity clipping and normalization, segmentation integration, and metadata validation, and is implemented in a reproducible, notebook-based framework compatible with common radiomics and deep learning workflows. This pipeline standardizes imaging data into analysis-ready volumes with consistent geometry, intensity distributions, and spatial alignment, reducing non-biological variability that can adversely affect radiomic feature stability and model performance. The modular design enables task-specific adaptation of individual preprocessing steps while maintaining overall consistency. Although demonstrated on TCIA, this framework is generalizable to other heterogeneous imaging datasets and provides a foundation for robust, large-scale computational imaging studies.

Urinary stone disease is a common urological condition with increasing incidence, particularly in developed countries. Laser lithotripsy (LL) has become a preferred minimally invasive treatment due to its high precision and low tissue damage. Recent studies suggest that cavitation plays a critical role in stone damage during LL, and three-dimensional passive cavitation mapping (3D-PCM) has emerged as a promising tool for detecting these events. However, clinical translation of 3D-PCM remains challenging due to limitations in imaging depth, field of view (FOV), and procedural compatibility. Here, we present a large-FOV dual-modality imaging system (3D-PCM and B-mode ultrasound) based on a large-aperture planar ultrasound array. Through array optimization and model-based reconstruction, our system achieves an expanded FOV of ~40*40mm^2 at a clinically relevant imaging depth of ~110mm, while maintaining high spatial resolution of ~0.6 mm laterally and ~0.4 mm axially. In vivo experiments in a porcine model demonstrate that the reconstructed cavitation distribution correlates well with stone damage. Our technology has the potential to provide real-time treatment feedback during LL without disrupting the standard workflow.

This study presents ReCo (Research Cosmos), a self-configuring and self-extending agentic research framework for the biomedical domain. ReCo is orchestrated by a large language model that interacts with native computing tools, bundled Model Context Protocol (MCP) servers, structured skills, persistent project memory, and a desktop interface. Its bundled MCP servers provide biomedical analysis capabilities while serving as implementation paradigms for integrating new computational and AI frameworks. Structured skills encode procedures for environment configuration and framework ingestion, enabling ReCo to inspect repositories, manuscripts, or local codebases; identify dependencies and execution patterns; create isolated runtime environments; design and implement MCP interfaces. Self-extension was evaluated using five heterogeneous systems: the Merlin computed tomography foundation model, MAISI-v2 medical image synthesis framework, asari liquid chromatography-mass spectrometry workflow, DosimeTron agentic radiation-dosimetry platform, and Orthanc DICOM server. ReCo successfully operationalized all five systems and completed predefined functional evaluations. Re-hosted DosimeTron outputs demonstrated near-perfect agreement with the reference pipeline across 651 organ observations (Pearson correlation and Lin concordance correlation coefficient, 0.99999; mean absolute percentage difference, 0.37%). Notably, ReCo configured Orthanc as a PACS-like coordination layer, integrated it with DosimeTron, Merlin, and TotalSegmentator, and orchestrated data retrieval, analysis, and return of valid DICOM RTSTRUCT, RTDOSE, and Structured Report. ReCo provides a unified environment for configuring, documenting, and operationalizing heterogeneous biomedical frameworks, reducing technical barriers to the adoption and integration of emerging computational and AI methods. The official open-source ReCo GitHub repository is available at: https://github.com/eltzanis/ReCo

Minimally invasive surgery is a powerful technique that enables operations deep within the body while minimizing patient trauma and recovery time. Optical endoscopes are key to providing intraoperative vision but still face challenges due to the loss of essential senses, including depth perception and tactile feedback for tissue evaluation. Thus, it is critical to develop endoscopic imaging technologies that can augment operators with critical information. In this work, we explore a prototype multimodal 3D imaging endoscope that integrates volumetric light-field imaging with laser-speckle contrast imaging to simultaneously capture 3D structure and blood-flow information in a clinically relevant form factor.

Purpose: RF-induced heating remains a major barrier to MRI access for patients with epicardial cardiac implantable electronic devices (CIEDs). Although ISO/TS 10974 Tier-3 transfer function (TF) methods are established for unbranched leads, no analogous framework exists for bifurcated leads, in which branch asymmetry and inter-branch coupling may substantially alter heating. We developed and validated a cumulative transfer function (cTF) framework to address this gap. Methods: Following ISO/TS 10974 Tier-3 formalism, we measured, calibrated, and validated cTFs for a commercial 35 cm bipolar epicardial lead at 1.5 T. The framework explicitly accounts for branch-specific response and cross-branch coupling. Validation was performed with 24 canonical lead configurations in a homogeneous phantom and, without recalibration, in a heterogeneous anthropomorphic pediatric phantom with clinically derived trajectories. A single-branch TF approximation served as a comparator. The validated cTF was applied to predict RF heating across adult and pediatric human models at multiple imaging landmarks. Results: Compared with the single-branch TF approximation, the cTF reduced prediction error by nearly 70% in the primary validation dataset. In secondary validation, the cTF maintained low error across clinically relevant trajectories and imaging landmarks. In human models, the framework revealed marked anatomy- and landmark-dependent variation in predicted heating for the tested 35 cm lead, with low predicted heating in pediatric models and substantially higher heating in selected adult chest and upper abdominal imaging scenarios. Conclusion: The cTF provides a validated framework for RF-heating assessment of bifurcated leads and substantially improves prediction accuracy over single-branch TF approximations that neglect branch coupling.

AO_SCPLOWBSTRACTC_SCPLOWEarly tumor detection is critical for improving patient survival and recovery. Clinically, tissue palpation is routinely used to identify regions of abnormal stiffness, a hallmark of many pathological conditions. However, palpation is restricted to anatomically accessible sites and remains highly operator dependent. Here, we introduce a method for real-time quantitative stiffness mapping using an unmodified commercial endoscope, with the goal of enhancing diagnostic capabilities and restoring mechanical feedback during endoscopic procedures. Our approach combines shear wave elastography with speckle imaging and an innovative synchronization strategy that enables the measurement of shear wave propagation using an unmodified commercial endoscope. The resulting wave fields are analyzed with the noise-correlation-inspired (NCI) method[1], providing pixel-wise estimates of shear wave velocity and, consequently, quantitative maps of local tissue stiffness. The method demonstrated robust performance in both benchtop and endoscopic configurations. Validation was achieved on polymer phantoms as well as on ex vivo and in vivo biological tissues, highlighting its potential for minimally invasive biomechanical imaging and real-time tissue characterization.

Deep learning models deployed in clinical imaging frequently encounter distribution shifts, yet most out-of-distribution (OOD) detection methods are evaluated only on controlled research datasets. As a result, it is unclear whether existing approaches can reliably identify segmentation failures that arise in real-world clinical practice. We evaluated six OOD detection methods on a deployed liver CT segmentation model (3D nnU-Net) using internal data from 400 patients and external data from 100 patients collected across nearly 70 sites in 7 countries. One method was Pairwise Surface DSC, a surface-based extension of Pairwise DSC, that we introduced. OOD performance was measured using sensitivity, AUROC, and balanced accuracy, with thresholds determined on an independent cohort of 400 patients using the Youden J statistic. Statistical significance was assessed using McNemar tests and stratified bootstraps ( = 0.05) with Benjamini-Hochberg correction. Pairwise Surface DSC was the top-performing method, with perfect sensitivities (1.00), near-perfect AUROCs (0.97 internal; 1.00 external), and the highest balanced accuracies (0.94 internal; 0.88 external; p<0.001). These results show that automated failure detection for liver CT segmentation is clinically feasible and that Pairwise Surface DSC is a promising candidate for deployment. Our code is available at https://github.com/mckellwoodland/liver_ct_ood_translation.