Physics in medicine and biology最新文献

{"title":"Is segmentation solved? An evaluation of vision foundation models for head and neck tumor segmentation.","authors":"Maria Baldeon Calisto, Noel Pérez-Pérez, Cesar Byron Guevara Maldonado","doi":"10.1088/1361-6560/ae6af8","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6af8","url":null,"abstract":"<p><strong>Objective: </strong>Vision foundation models (FMs) have shown strong potential for adapting to diverse downstream tasks without specialized fine-tuning. However, their performance in head and neck cancer (HNC) tumor segmentation remains insufficiently explored. This work provides a comprehensive evaluation of zero-shot and few-shot performance of five prominent FMs for HNC tumor segmentation. &#xD;Approach: The FMs tested include SAM, MedSAM, nnInteractive, UniverSeg, and SegGPT. The evaluation of SAM, MedSAM, and nnInteractive utilized a bounding box prompting strategy. The influence of prompt precision is assessed by systematically enlarging the prompt bounding box until it reaches twice the size of the tightest bounding box enclosing the region of interest. For UniverSeg and SegGPT, the prompt consisted of example image-label pairs, and the effect of increasing the size of this set is tested. Two-factor repeated measures ANOVAs were used for statistical analysis, confirming that both the FM architecture and the prompting strategy have a statistically significant effect on segmentation performance for both primary tumors (GTVp) and metastatic lymph nodes (GTVn).&#xD;Main Results: For SAM, MedSAM, and nnInteractive larger bounding boxes lead to notable performance degradation. Moreover, MedSAM outperformed SAM and nnInteractive for GTVp segmentation, whereas SAM provided slightly better results than the competitors for GTVn segmentation. For UniverSeg, performance improved as more image-label pairs are added to the support set, while SegGPT remained unaffected by this factor. Overall, UniverSeg and SegGPT demonstrate limited few-shot capability, with low mean dice scores across all scenarios. Comparisons against supervised CNN models trained specifically for HNC segmentation showed that conventional architectures remain more stable and clinically reliable. &#xD;Significance: These findings highlight the ongoing challenges faced by current FMs in achieving reliable zero- and few-shot medical segmentation. The results emphasize the critical need for improved medical-domain adaptation and enhanced prompt robustness to advance the clinical utility of FMs in oncology.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147857008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MARTP: a multi-agent simulation framework for automated radiation therapy planning based on LLMs.","authors":"Dongzhao Wang, Zeyun Hu, Yang Li, Dachuan Xu, Ruijie Yang, Changjing Zhuge","doi":"10.1088/1361-6560/ae6af6","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6af6","url":null,"abstract":"<p><strong>Objective: </strong>High-quality radiotherapy requires accurate dose delivery to target volumes while protecting organs-at-risk (OARs). However, current clinical workflows remain constrained by labor-intensive multidisciplinary collaboration, prolonged planning cycles, and limited scalability. Intelligent automation capable of integrating clinical knowledge and real-world decision patterns is needed to enhance precision and efficiency in radiotherapy planning. This study proposes MARTP, a textbf{M}ulti-textbf{A}gent textbf{R}adiation textbf{T}herapy textbf{P}lanning framework driven by large language models (LLMs), to emulate multidisciplinary clinical workflows and enable end-to-end intelligent radiotherapy planning and evaluation.&#xD; Approach: MARTP coordinates five specialized agents to integrate data analysis, weight adjustment, plan optimization, plan evaluation, and report generation into a unified radiotherapy planning workflow. The framework leverages supervised fine-tuning (SFT) on expert weight adjustment demonstrations to improve adaptation to complex clinical cases, incorporates retrieval-augmented generation (RAG) to ground planning decisions in case-specific knowledge, and employs a predefined model-context protocol (MCP) to enable high-precision treatment plan generation. In addition, reinforcement learning (RL) with expert preference data is used to develop an intelligent plan evaluation mechanism.&#xD;Results: Statistical analysis reveals that the dosimetric metrics of plans generated by MARTP exhibit no statistically significant differences compared with expert-crafted clinical plans, while the planning efficiency is substantially improved. In addition, the framework demonstrates robust and safe behavior under abnormal input scenarios, maintaining clinically acceptable outputs. The SFT and RL components further enhance the consistency, semantic accuracy, and reliability of model-generated weight adjustments and plan evaluations.&#xD;Significance: MARTP demonstrates that LLMs-driven multi-agent systems can effectively replicate multidisciplinary radiotherapy workflows, generating clinically comparable plans with substantially improved efficiency. The framework provides a promising pathway for integrating intelligent automation into radiotherapy practice, supporting more consistent decision-making and scalable treatment planning.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of daily volumetric imaging on target tracking without fiducial markers during robotic treatment of pancreas.","authors":"James L Bedford, Simeon Nill, Uwe Oelfke","doi":"10.1088/1361-6560/ae6229","DOIUrl":"10.1088/1361-6560/ae6229","url":null,"abstract":"<p><p><i>Objective.</i>Treatment of pancreas using Cyberknife is usually accomplished using radio-opaque fiducial markers that can be visualised on the orthogonal kilovoltage imaging system, but implantation of markers can lead to complications. This study therefore describes a method for pancreas tracking without fiducial markers and estimates the improvement in accuracy that can be achieved by additional daily pre-treatment volumetric imaging.<i>Approach.</i>Fiducial markers were artificially removed from the digitally reconstructed radiographs and treatment images of eight previously treated patients. Pancreas position was identified using a combination of local rigid registrations and inter- and intra-fraction motion models. The optimum effect of daily volumetric imaging was simulated by correcting the mean tracking position to match the mean position observed using fiducials. The impact of residual error on the dose distribution was estimated by shifting and recalculating the treatment plan.<i>Main results.</i>Median absolute tracking accuracy without fiducials over the 2823 acquired images is 3.8 mm (range 0.2-19.9 mm) without daily volumetric imaging. The lower bound of accuracy is 2.3 mm (0.1 mm-18.5 mm) when daily volumetric imaging is used. The former scenario reduces the<i>D</i>_95%of the planning target volume by a median of 2.5 Gy (1.0-10.2 Gy) compared to the planned dose distribution, whereas the latter scenario reduces<i>D</i>_95%by 1.4 Gy (0.4-2.8 Gy). In neither case is the<i>D</i>_95%of the clinical target volume (CTV) substantially lowered below the prescription. Median<i>D</i>_0.5ccto critical structures changes by less than 2 Gy.<i>Significance.</i>When tracking the target without fiducial markers, a planning target margin of 3-5 mm is sufficient to ensure that the CTV receives the desired prescribed dose, but the spatial accuracy and target coverage are considerably improved by using daily volumetric imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noise-aware adaptive diffusion sampling for accelerated knee MRI reconstruction.","authors":"Dabin Kim, Hongki Lim","doi":"10.1088/1361-6560/ae6af5","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6af5","url":null,"abstract":"<p><p>We present Noise-Aware adaptive Diffusion sampling (NAD), a novel approach combining classical noise estimation method with diffusion models for accelerated MRI reconstruction. NAD incorporates a noise estimation step based on patch-based Principal Component Analysis (PCA) step that produces a data-consistent least-squares reconstruction as the starting point-thus enabling informed initialization-and guides adaptive sampling in the diffusion process. The method incorporates conjugate gradient-based data consistency updates and controlled noise injection, meaning it re-injects Gaussian noise calibrated to the estimated noise level $hat{sigma}(t)$ and scaled by $gamma$ to efficiently explore the solution space. Evaluated on the Stanford Knee MRI dataset, NAD consistently outperforms state-of-the-art diffusion-based methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), while significantly reducing computational time. The proposed method not only advances accelerated MRI reconstruction but also provides insights into efficiently leveraging diffusion models for inverse problems in medical imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ScatterFusionNet: physics-informed deep scatter correction for dual-detector CT using Klein-Nishina prior.","authors":"Huahai Sun, Wenyu Zhang, Liang Li","doi":"10.1088/1361-6560/ae6af7","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6af7","url":null,"abstract":"<p><strong>Objective: </strong>Scatter artifacts degrade cone-beam CT image quality, yet acquiring ground-truth scatter-free data in clinical settings requires time-consuming measurements. Purely data-driven deep learning methods exhibit limited generalization across anatomical regions, often learning anatomy-specific shortcuts rather than fundamental scattering physics. We aim to develop a physics-informed scatter correction framework that enables robust cross-anatomy generalization without extensive site-specific training data.&#xD;&#xD;Approach: We propose ScatterFusionNet, a physics-informed neural network that incorporates Klein--Nishina scattering priors to embed angular scattering constraints into the learning process. The network fuses side-detector measurements from dual-detector CT with a multi-scale backbone via Feature-wise Linear Modulation (FiLM), where Klein--Nishina prior maps guide feature modulation in a physically grounded manner. The model is trained on Monte Carlo simulations and fine-tuned using a single right-ear dataset.&#xD;&#xD;Main results: When evaluated on unseen right-teeth and left-teeth datasets, the proposed method achieves 5.7% and 3.6% CNR improvements, respectively, closely matching beam stop array ground truth. Under identical training protocols, a classical SE UNet baseline shows only marginal gains (0.8% and 1.0%), indicating substantially weaker cross-anatomy generalization.&#xD;&#xD;Significance: These results demonstrate that embedding physics-informed priors into deep networks is critical for building robust scatter correction systems. By integrating Klein--Nishina constraints with dual-detector measurements, the proposed framework enhances generalizability across anatomical sites while reducing dependence on extensive anatomy-specific training data.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic models for ultrasound-switchable fluorescence.","authors":"Baohong Yuan","doi":"10.1088/1361-6560/ae639f","DOIUrl":"https://doi.org/10.1088/1361-6560/ae639f","url":null,"abstract":"<p><p><i>Objective.</i>Near-infrared fluorescence imaging enables deep-tissue visualization but is limited to millimeter-scale resolution due to photon scattering. Ultrasound-switchable fluorescence (USF) improves resolution by thermally activating fluorophores within a focused ultrasound region. Although experimentally demonstrated, quantitative understanding of the coupled acoustic-thermal-fluorescence-optical processes governing USF signal formation remains limited. This work establishes a dynamic modeling framework to describe USF signal and velocity formation and to explore model-driven strategies for improving imaging performance.<i>Approach.</i>A physics-based framework integrating ultrasound pressure and heating, temperature-dependent fluorescence quantum yield, and photon diffusion was developed. Both analytical derivations and numerical simulations were performed to investigate the dynamic behavior of USF signal strength and velocity under different target configurations and optical source-detector geometries.<i>Main results.</i>The framework reproduces the dynamic evolution of USF signal strength and velocity. Analytical expressions were derived to estimate separation limits of adjacent targets within the ultrasound focal volume. Velocity-based analysis reveals structural information beyond conventional intensity-based imaging. A time-dependent sensitivity matrix was obtained, indicating improved spatial localization potential for tomographic reconstruction.<i>Significance.</i>This study provides a quantitative theoretical basis for dynamic USF imaging and highlights the additional structural information contained in signal velocity. The results suggest that velocity-based analysis may enable differentiation of otherwise indistinguishable features within the focal volume. The derived dynamic sensitivity matrix further supports tomographic reconstruction and may facilitate future deep-tissue super-resolution strategies.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":"71 9","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147841440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proof of concept for full-waveform inversion in ultrasound time-harmonic shear-wave elastography.","authors":"Mohamed Aziz Boukraa, Yücel Karabiyik, Andreas Austeng, Sverre Holm, Sven Peter Näsholm","doi":"10.1088/1361-6560/ae6af4","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6af4","url":null,"abstract":"<p><strong>Objective: </strong>In ultrasound time-harmonic elastography, tissue elasticity is estimated from the ultrasound-measured shear-wave displacement fields, generated by an external vibrator. Commonly used techniques to map from the ultrasound pulse-echo data to tissue elasticity maps, such as the phase-gradient and local-frequency estimation methods, base their estimates on a set of displacement field data and do not involve direct numerical modeling of the underlying shear-wave propagation. In the current proof-of-concept study, the objective is to develop a Full-Waveform Inversion (FWI) approach to ultrasound-based time-harmonic elastography, providing a physics-driven framework for improved tissue elasticity estimation.</p><p><strong>Approach: </strong>FWI is a methodology commonly applied in geophysics. It relies on simulating the wave propagation and scattering in heterogeneous media to model the intricate relationship between the observed wavefield and the propagation medium properties. Our FWI-based tissue-harmonic elastography approach is based on minimizing a cross-correlation based objective function that quantifies phase mismatches between measured and simulated shear waves.</p><p><strong>Results: </strong>Experiments on synthetic phantom data obtained from the fractional Kelvin-Voigt wave model demonstrate that our approach accurately reconstructs the shear wave speed of the medium, even in the presence of significant noise, and without relying on pre-filtering steps such as directional filtering, which selects waves based on their propagation direction. An improvement of approximately 0.4 m.s -1 within the inclusion region, compared to an established method, is observed for a noisy wavefield with decreasing signal-to-noise ratio, ranging from 20 to -10 dB from top to bottom of the ultrasound image. In addition, the reconstructed shear-wave speed map achieved a 50% reduction in standard deviation.</p><p><strong>Significance: </strong>This work demonstrates the potential for enhanced elasticity mapping using FWI-based reconstruction in time-harmonic elastography with prospective impacts for future clinical applications.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A low-cost, flexible, and accurate technique to study new immobilisation systems for radiotherapy and their 2D in-plane positional reproducibility and stability.","authors":"Chung Tin Lo, Jamie R McClelland, Tracy Underwood","doi":"10.1088/1361-6560/ae6afa","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6afa","url":null,"abstract":"<p><p>Recently, there has been a resurgence of interest in upright, gantry-less radiotherapy due to its potential to reduce the costs associated with new treatment rooms and improve comfort/clinical outcomes for specific patient groups. Consequently, new patient positioning and immobilisation devices are being developed for upright and conventional radiotherapy. Before any new RT immobilisation system is implemented clinically, pre-clinical tests should be performed to verify its reproducibility and stability. Surface-guided radiotherapy (SGRT) can be used; however, commercial SGRT systems are expensive and can require complex installation and calibration procedures. Therefore, we developed a low-cost, flexible 2D in-plane displacement measurement system to support the testing and development of new immobilisation devices for RT. The method developed in this work couples ArUco markers with regular cameras positioned orthogonally on tripods to capture images in two planes. Markers fixed onto human subjects can then be used to assess the inter- and intra-fractional repeatability and stability associated with new immobilisation systems. A Python algorithm was developed to automatically detect ArUCo markers in each camera image and infer the distance between markers across multiple camera images. Contrary to most measurement systems, the proposed method does not require camera calibration, and any camera with sufficiently high image resolution and relatively low lens distortion can be used. Rigorous validation experiments, using simulated data, were performed to test the algorithm's measurement accuracy for different marker separations and camera-to-marker distances. Accuracy validation test results show that the proposed system has a mean measurement error below 1mm. These errors are at the same order of magnitude as the commercial SGRT systems. The proposed system provides a viable low-cost solution for assessing positional repeatability and stability of new immobilisation devices during pre-clinical development. This is particularly relevant as new positioning systems are being developed for both upright and conventional supine RT treatments.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A delta-based multi-omics model to predict adaptive radiotherapy benefit in patients with non-small cell lung cancer during carbon-ion radiotherapy.","authors":"Zhuojun Ju, Nobuteru Kubo, Xiangdi Meng, Makoto Sakai, Mutsumi Tashiro, Tatsuya Ohno","doi":"10.1088/1361-6560/ae64a6","DOIUrl":"10.1088/1361-6560/ae64a6","url":null,"abstract":"<p><p><i>Objective.</i>Patients with non-small cell lung cancer (NSCLC) treated with carbon-ion radiotherapy (CIRT) are potential candidates for adaptive radiotherapy (ART). However, ART is time- and resource-intensive. This study aimed to develop a decision-support tool to estimate ART demand before treatment.<i>Approach.</i>This retrospective study included 69 patients with NSCLC treated with CIRT. Baseline clinical characteristics, dose-volume histogram (DVH) parameters, and radiomic and dosiomic features from the plan-computed tomography (CT) (1-2 weeks before treatment) and confirm-CT (1-2 d before treatment) were collected, and relative change rates in features (ΔDVH, Δradiomics, and Δdosiomics) were calculated. Feature selection involved univariable testing, correlation analysis, least absolute shrinkage and selection operator regression, and stepwise regression with 1000 bootstrap resamples. A multivariable logistic regression model was constructed and validated using 200 iterations of five-fold cross-validation.<i>Main results.</i>ART was triggered in 32 (46.4%) patients. A five-feature delta-omic model (one ΔDVH, two Δradiomic, and two Δdosiomic features) achieved a validated median area under the curve of 0.897 (interquartile range, 0.833-0.950) with good calibration. At a high-sensitivity threshold (0.11), the model identified all ART candidates while potentially reducing the frequency of weekly-CT evaluations in 21.4% of patients; at the maximum-Youden threshold (0.41), the median accuracy reached 78.6%, with up to 50.0% classified as low ART demand.<i>Significance.</i>A delta-omic model predicting ART demand in patients with NSCLC receiving CIRT was developed. The model may help optimize resource allocation and streamline ART workflows, although prospective validation in larger cohorts is warranted before widespread clinical adoption.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147778467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cryoablation temperature monitoring with dense ultrasonic speed-of-sound shift imaging.","authors":"Gaya Lamm, Tal Grutman, Mike Bismuth, Tali Ilovitsh","doi":"10.1088/1361-6560/ae6af9","DOIUrl":"https://doi.org/10.1088/1361-6560/ae6af9","url":null,"abstract":"<p><strong>Objective: </strong>To accurately monitor temperature during cryoablation, a minimally invasive technique that destroys tissue locally by forming an ice ball around an inserted cryoprobe.&#xD;Approach: We present a dense slowness-shift imaging method that estimates local speed-of-sound changes from ultrasound B-mode images using optical flow. This single-transducer, image-based approach enables mapping of spatial temperature change without requiring additional hardware. Cryoablation experiments were conducted in a tissue-mimicking phantom and ex vivo turkey breast.&#xD;Main results: Slowness deviation increased with decreasing temperature. In the phantom, the dependence was linear (αₐ = -20.70 ηs·m⁻¹C°⁻¹), while in turkey breast it followed an exponential relationship (αₜ = 34.04×exp(0.075(-ΔT)-1) ηs·m⁻¹C°⁻¹). The algorithm detected sub-degree temperature variations and accurately tracked cooling down to -39.4 ± 5.6 °C.&#xD;Significance: Accurate temperature monitoring is essential for effective and safe cryoablation. This work demonstrates the feasibility of ultrasound-based, noninvasive temperature monitoring during cryoablation, providing a scalable, real-time alternative to existing invasive or high-cost thermal assessment techniques.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}