Physics in medicine and biology最新文献

{"title":"Nonlinear stability phenotypes and collapse dynamics of multi-organ radiomics in total-body PET imaging: toward digital twin-oriented robustness assessment.","authors":"Mohammad Saber Azimi, Maryam Cheraghi, Habib Zaidi","doi":"10.1088/1361-6560/aeb699","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb699","url":null,"abstract":"<p><strong>Objective: </strong>Radiomics analysis in PET offers a high-dimensional representation of tissue characteristics, yet its reliability is limited by sensitivity to acquisition perturbations, particularly dose reduction. This study aims to introduce a nonlinear stability phenotyping framework to characterize dose-response behavior of multi-organ radiomics and evaluate their potential relevance for future digital twin-oriented applications.&#xD;Approach: A multi-center dataset of 550 total-body PET scans was analyzed. Dose reduction was simulated using list-mode subsampling (dose reduction factor - DRF = 2-100). A total of 107 radiomics features were extracted per organ. Stability was quantified using Intraclass Correlation Coefficient (ICC) and summarized as stability fractions. Nonlinear models were fitted to organ- and feature-level trajectories. Stability phenotypes and critical thresholds were derived, along with organ-level instability metrics.&#xD;Main results: Radiomics stability exhibited strongly nonlinear, organ-dependent behavior. Feature-level analysis identified frequent sigmoid-associated collapse patterns with high descriptive model fits (R²>0.90). The liver showed the highest instability burden (30.8% unstable features), followed by esophagus and kidneys (18-20%). Texture features were the primary source of instability, whereas shape features remained robust. Organ-level trajectories demonstrated heterogeneous responses, with some organs maintaining high stability (0.80-0.95) across DRFs, while others showed sharp declines (down to 0.64). Linear (stable) behavior dominated at the organ level, though masking underlying nonlinear feature dynamics.&#xD;Significance: Radiomics stability in total-body PET follows nonlinear, phenotype-driven dynamics with identifiable tipping points and organ-specific sensitivity. The proposed framework provides a behavior-based approach for evaluating radiomics robustness and supports the selection of reliable features and organs for robust quantitative imaging and may inform future digital twin-oriented applications.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149228627","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":"Magnet-free proton therapy with 4D pencil beam delivery optimisation.","authors":"Nair N von Mühlenen, Florentin Bieder, Ye Zhang, Philippe C Cattin","doi":"10.1088/1361-6560/aeb103","DOIUrl":"10.1088/1361-6560/aeb103","url":null,"abstract":"<p><p><i>Objective</i>. Motion management is a critical challenge in proton therapy for mobile tumours. This study aims to develop and evaluate a novel four-dimensional (4D) pencil beam delivery strategy that incorporates respiratory motion into a dynamic treatment plan to improve dose conformity and treatment efficiency.<i>Approach</i>. To assess this 4D pencil beam delivery strategy, a mobile phantom was used. The generated 4D treatment plans were assessed with various scanner configurations, including gantry-less and magnet-free scanner heads. For each setup, the treatment time, dose conformity, and robustness against irregular breathing patterns were quantified. The influence of scanner head design and patient-specific motion irregularities on overall plan quality was evaluated.<i>Main Results</i>. The 4D planning tool generated treatment plans that achieved clinically acceptable dose distributions across all configurations. In magnet-free configurations, static beam operation substantially increased treatment time and reduced dose conformity. In contrast, configurations using a single scanner magnet, without a gantry, maintained acceptable conformity within practical treatment times.<i>Significance</i>. The proposed 4D delivery strategy demonstrates feasibility for treating mobile targets with simplified, gantry-less and magnet-free scanner designs. Further improvements could be achieved by synchronising the patient's breathing with 4D delivery, which may enhance dose accuracy during irregular or interrupted breathing. By reducing system complexity while preserving dosimetric performance, this approach offers a pathway toward more accessible and cost-effective proton beam therapy for motion-affected tumours. Our code is available on.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149112147","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":"Generative modeling of complex-valued brain MRI data.","authors":"Marco Schlimbach, Moritz Rempe, Jessica Mnischek, Lukas T Rotkopf, Jens Weingarten, Jens Kleesiek, Kevin Kröninger","doi":"10.1088/1361-6560/aeb088","DOIUrl":"10.1088/1361-6560/aeb088","url":null,"abstract":"<p><p><i>Objective.</i>Standard magnetic resonance imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite its sensitivity to tissue properties that are not represented by magnitude alone. The aim of this study is to develop a generative framework that jointly models magnitude and phase information in complex-valued brain MRI data and enables the conditional generation of MRI samples representing normal and abnormal brain tissue.<i>Approach.</i>The proposed generative framework combines a conditional variational autoencoder, which compresses complex-valued MRI scans into latent representations while preserving phase coherence, with a flow-matching-based generative model. Synthetic sample quality is assessed using a real-versus-synthetic classifier, image-space coverage tests, and downstream classifiers trained on synthetic data for abnormal tissue detection.<i>Main results.</i>The autoencoder preserves phase coherence above 0.997. Real-vs-synthetic classifiers yielded low AUROCs between 0.53 and 0.64 for generated phase images supporting realistic phase modeling. Magnitude was moderately distinguishable, with AUROCs between 0.53 and 0.82, suggesting appreciable overlap between real and synthetic sample distributions while also indicating a distribution shift for several sequences. Image-space coverage was broad. On the held-out test split, no consistent directional difference from the matched real-to-real references was detected in any evaluated setting, while the training split showed slight to moderate undercoverage depending on acquisition sequence and tissue condition. In downstream normal-versus-abnormal classification, classifiers trained entirely on synthetic data achieve an AUROC of 0.880, surpassing the real-data baseline of 0.842 on the fastMRI dataset. This advantage persists on an independent external test set with biopsy-confirmed labels.<i>Significance.</i>The proposed framework demonstrates the feasibility of jointly modeling magnitude and phase information for normal and abnormal complex-valued brain MRI data. Beyond synthetic data generation, the framework enables future investigation of how magnitude and phase jointly encode pathology-specific features once sufficiently large raw MRI datasets with fine-grained, pathology-confirmed labels become available.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149113741","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":"Variability and bias in the calculation of iodine uptake with an adaptive background subtraction method in contrast-enhanced mammography.","authors":"Jorge Patricio Castillo-Lopez, Liliana Moreno Astudillo, Yolanda Villaseñor-Navarro, María-Ester Brandan","doi":"10.1088/1361-6560/aeaac1","DOIUrl":"10.1088/1361-6560/aeaac1","url":null,"abstract":"<p><p><i>Objective.</i>This work evaluates variability and bias in the calculation of iodine uptake from contrast-enhanced dual-energy mammography (CEM) images using an adaptive background subtraction method.<i>Approach.</i>The iodine uptake images were custom-generated using weighted subtraction, followed by subtraction of a smooth background estimated by interpolation from neighboring regions outside the lesion (adaptive background subtraction). Three main sources of variability and bias were assessed: differences in breast tissue linear attenuation coefficients, stability of the mammography system, and intra- and inter-observer variability in lesion segmentation. Tissue-related effects were evaluated theoretically. System stability was assessed using weekly phantom measurements acquired over a 4-year period. For observer variability, four radiologists performed two segmentation rounds of iodine-enhancing regions in 40 CEM studies. Typical uptake values were below 5 mg cm^-2. Whenever possible, variability was expressed as the 95% coverage interval (2σ), where σ represents the standard deviation<i>. Main results.</i>Theoretical evaluation showed that tissue composition differences may introduce biases in iodine uptake exceeding 0.5 mg cm^-2, although typical lesion compositions produce biases below the visual detection threshold. Long-term phantom measurements demonstrated excellent system stability over four years (2σ ≈0.2 mg cm^-2, and limit of detection = 0.24 mg cm^-2), with a 7% change in calibration following a workstation upgrade. In the clinical dataset, the four radiologists achieved high detection agreement when segmenting the primary lesions and greater variability for secondary lesions; the inter-observer variability in mean uptake was approximately 2σ ≈0.6 mg cm^-2.<i>Significance.</i>These findings indicate that iodine uptake quantification in CEM using an adaptive background subtraction method is technically robust, which is a key element for exploring its potential usefulness as an imaging biomarker.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017759","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 fast and reliable quality assurance framework for CBCT-based synthetic CTs for adaptive proton radiotherapy.","authors":"Arthur Galapon, Andreas Johan Smolders, Dirk Wagenaar, Stefan Both, Francesca Albertini","doi":"10.1088/1361-6560/aeb645","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb645","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Cone beam CT based synthetic CTs (sCTs) enable online adaptive proton therapy by preserving daily patient anatomy without additional acquisition of diagnostic-quality CTs. However, clinical adoption is limited by the lack of fast, reliable quality assurance (QA) methods. This study proposes and evaluates a practical QA workflow to assess sCT suitability for proton dose optimization. &#xD;Approach:&#xD;A two-step sCT-QA workflow was developed in which a candidate sCT intended for reoptimization (sCTref) is compared with an independently generated secondary sCT (sCTtest). The workflow combines image based metrics and dose based metrics, with doses derived from recalculating a template proton plan on both sCTs. In this study, the framework was evaluated using a deformably propagated virtual CT (vCT) as sCTref and either a deep learning-based sCT (sCTDL) or an analytically corrected CBCT (CBCTcorr) as sCTtest. Same-day CTs were deformably registered to the CBCT and used as ground truth (CTGT) for evaluation. Thresholds were optimized using 5-fold cross-validation on 182 head and neck (H&N) CT-CBCT pairs. Dose calculation suitability was defined as a gamma pass rate (2%, 1mm) exceeding 95% between dose distributions computed on sCT and CTGT. The method was further tested on an external H&N dataset and on abdominal cases.&#xD;Main Results:&#xD;Optimal classification performance was achieved using a combination of image mean absolute error (MAE) and dose MAE. On the H&N test set, the workflow achieved balanced accuracies of 91.2% and 89.7% for the vCT-CBCTcorr and vCT-sCTDL comparisons, respectively, while maintaining a low false positive rate. When applied without modifications to the external H&N and abdominal datasets, balanced accuracy was 94.7% (HN) and 85.0% (abdominal) for vCT-CBCTcorr, and 37.9% (HN) and 61.1% (abdominal) for vCT-sCTDL.&#xD;Significance:&#xD;This study introduces a clinically feasible sCT QA workflow for CBCT based adaptive proton therapy and showed promising transferability across datasets.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224698","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":"Dimensionality reduction for partial-volume correction in quantitative SPECT.","authors":"Johan Gustafsson, Erik Larsson, Katarina Sjogreen-Gleisner","doi":"10.1088/1361-6560/aeb644","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb644","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this paper is to develop a method for region-based post-reconstruction partial-volume correction (PVC) of SPECT images, which explicitly estimates the mean activity concentration in regions without assuming strict intra-regional uniformity.</p><p><strong>Approach: </strong>Partial-volume correction was applied to¹⁷⁷Lu-SPECT images obtained from the simulation of eight anthropomorphic phantoms as well as two example patients. Both sets of data were reconstructed using OS-EM with ten subsets and up to 40 iterations, including compensation for attenuation, scatter, and spatial resolution. Reconstructed images were deconvolved using the van Cittert algorithm and Lucy-Richardson algorithm, both implemented on images represented as standard cuboid voxels or as regional voxels, consisting of regions defined from whole-image segmentation. Methods embedding regional voxels were applied both assuming region-wise uniform activity concentration and incorporating perturbations in the form of cosine series to relax the uniformity assumption. For comparison, images were also processed with the iterative Yang method and MIRD recovery coefficients. Evaluation was performed with respect to the estimated activity concentration for kidneys, spleen, and tumours, as well as the total activity in the images.</p><p><strong>Main results: </strong>Mean activity-concentration errors prior to PVC were (-22.6±11.5) %, (-13.8±2.1) %, and (-7.4±2.8) % for tumours, kidneys, and spleen, respectively. Region-perturbed van Cittert deconvolution with order 4 reduced these errors to (2.3±7.7) %, (0.3±2.2) %, and (4.0±1.6) % while the errors for region-perturbed Lucy-Richardson deconvolution of order 4 were (0.3±8.0) %, (0.0±1.6) %, (2.6±0.5) %. Corresponding results for the iterative Yang method were (2.8±21) %, (-0.3±2.1) %, and (3.8±2.4) % and using MIRD recovery coefficients (-5.0±14.2) %, (-7.1±1.9) %, and (-0.6±3.1) %.</p><p><strong>Significance: </strong>Region-based PVC improves the accuracy of quantitative SPECT, and the inclusion of cosine perturbations allows for a gradual relaxation of the uniformity assumption.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224672","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":"Study on the capture and thrombolytic efficacy of magnetic particle clusters under a deeply focused rotating magnetic field.","authors":"Shoulong Dong, Zongnan Liu, Yuan Lei, Sizhe Xiang, Lisheng Zhao, Dengfeng He, Chenguo Yao","doi":"10.1088/1361-6560/aeb643","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb643","url":null,"abstract":"<p><p>Magnetic particle-mediated targeted delivery of tissue plasminogen activator (tPA), assisted by a rotating magnetic field (RMF), represents a highly promising approach for the treatment of venous thrombosis. However, existing magnetic strategies face bottlenecks in deep tissues and complex hemodynamics due to poor focusing and weak flow resistance. Consequently, flow washout induces particle escape, while magnetic attenuation severely limits the clusters ' operating range and fluid perturbation. Achieving deep focusing and flow-resistant retention of magnetic particle clusters (MPCs) under deep-tissue, high-flow conditions remains a critical challenge for the clinical translation of this technology. To this end, a novel deeply focused rotating magnetic field (DF-RMF) system driven by dual synchronized rotating permanent magnets is developed. It establishes an axial magnetic focusing zone with dual-peak confinement and continuous centripetal properties to effectively counteract fluid drag. Simulations and in vitro flow experiments demonstrate that the DF-RMF stably confines magnetic particle clusters as compact, continuously rotating clusters at the lumen center. This effectively expands their geometric working boundaries and significantly enhances synergistic thrombolytic efficacy. Even at distances up to 5 cm or under the normal physiological flow velocity of lower extremity veins (~14 cm/s), the system maintains a substantial particle retention rate. Rotating synchronously under the DF-RMF, the clusters generate mechanical shear while inducing intense microfluidic circulation and pressure fluctuations. This physically disrupts the thrombus and accelerates interfacial tPA transport to boost fibrinolysis, achieving complete in vitro recanalization with a removal rate ~5 times that of tPA alone. This study provides a novel physical modulation strategy with translational potential for deep, precision targeted delivery and synergistic mechanical-biochemical thrombolysis in complex blood flow environments.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224731","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":"Imaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods.","authors":"Tanxin Zhu, Emran Hossen, Chen Zhao, Jingfeng Jiang, Michele Esposito, Jiguang Sun, Weihua Zhou","doi":"10.1088/1361-6560/aeb642","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb642","url":null,"abstract":"<p><p>Purpose of Review Imaging-derived fractional flow reserve (FFR) is rapidly evolving beyond conventional computational fluid dynamics (CFD)-based pipelines toward machine learning (ML), deep learning (DL), and physics-informed approaches that enable fast, wire-free, and scalable functional assessment of coronary artery stenosis. This review synthesizes recent advances in computed tomography (CT)-and angiography-based FFR measurement, with particular emphasis on emerging physics-informed neural networks and neural operators (PINNs and PINOs), as well as key considerations for their clinical translation.Recent Findings ML/DL approaches have markedly improved automation and computational speed, enabling prediction of pressure and FFR from anatomical descriptors or angiographic contrast dynamics. However, their real-world performance and generalizability can remain variable and sensitive to domain shift, due to multi-center heterogeneity, interpretability challenges, and differences in acquisition protocols and image quality. Physics-informed learning introduces conservation structure and boundary-condition consistency into model training, improving generalizability and reducing dependence on dense supervision while maintaining rapid inference. Recent evaluation trends increasingly highlight deployment-oriented metrics, including calibration, uncertainty quantification, and quality-control gatekeeping, as essential for safe clinical use.SummaryThe field is converging toward imaging-derived FFR methods that are faster, more automated, and more reliable. While ML/DL offers substantial efficiency gains, physics-informed frameworks such as PINNs and PINOs may provide a more robust balance between speed and physical consistency. Prospective multi-center validation and standardized evaluation will be critical to support broad and safe clinical adoption.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224715","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":"Physicochemical modeling of electron FLASH to identify the roles of oxygen, aqueous electrons and causal radical recombination effects underlying them.","authors":"William S Thomas, Xu Cao, David J Gladstone, Harold M Swartz, Brian W Pogue","doi":"10.1088/1361-6560/aeabe5","DOIUrl":"10.1088/1361-6560/aeabe5","url":null,"abstract":"<p><p><i>Objective.</i>Ultra-high dose-rate (UHDR) irradiation reduces normal-tissue toxicity as compared conventional dose-rate irradiation, known as the FLASH effect, yet the underlying radiochemical mechanisms remain poorly understood. Direct measurements rely upon longer lived water-radiolysis species such as aqueous electrons (e_aq^-) and oxygen, to infer behaviors of toxic species leading to tissue damage. This study uses kinetic modeling to identify which chemical pathways exhibit dose-rate-dependent behavior and can be used to correlate experiments.<i>Approach.</i>A comprehensive physicochemical model of water radiolysis was developed, incorporating 16 molecular species and 56 coupled reaction rate equations, with pulsed-beam structures matched to published UHDR electron irradiation experiments. Simulations were performed across a range of oxygen tensions (0.1%-10% O₂) and dose-rate conditions (0.3-300 Gy s^-1). Model predictions were validated against experimental measurements of e_aq^-transients, and oxygen-consumption<i>in vitro</i>and<i>in vivo</i>. They also were compared to prior computational models.<i>Main results.</i>Modeled lifetimes of e_aq^-matched experiments across O₂concentrations and showed that >90% of scavenging occurs via O₂and reactions with proteins, with radical-radical recombination contributing <5% even at 300 Gy s^-1. The lifetimes of OH· radicals were similarly dominated by protein scavenging, with recombination pathways remaining minor (<5%) with UHDR. Modeled oxygen consumption matched the magnitude and dose-rate dependence observed in published experiments. The results indicated a primary dose-rate sensitive pathway, the reaction between hydroperoxyl radical (HO₂·) and superoxide (O₂·^-). Peroxyl-radical formation decreased at UHDR within oxygen tensions (≈5-20 mmHg) corresponding to the experimentally observed FLASH oxygen-window.<i>Significance.</i>This work identifies specific radiochemical pathways that exhibit true dose-rate sensitivity and clarifies which proposed FLASH mechanisms are unsupported by homogeneous-phase radical kinetics. Radical-radical recombination contributes too little (<5%) to account for known FLASH sparing at UHDR, whereas peroxyl-radical and HO₂·/O₂·^-chemistry shows dose-rate-dependent behavior within physiologic oxygen tensions associated with FLASH. Although constrained by simplified aqueous conditions lacking scavengers and tissue heterogeneity, the model provides mechanistic insight into the radiochemical regimes dominating the homogeneous phase chemistry.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149031219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural network for photoacoustic tomography reconstruction.","authors":"Zhan Shen, Hao Ding, Jiayi Sun, Qili Tian, Zhijun Chi, Qiao Li, Yingchao Du, Xiaoping OuYang, Chuanxiang Tang","doi":"10.1088/1361-6560/aeb59a","DOIUrl":"https://doi.org/10.1088/1361-6560/aeb59a","url":null,"abstract":"<p><strong>Objective: </strong>&#xD;Conventional photoacoustic tomography (PAT) reconstruction algorithms often suffer from performance degradation under complex media and non-ideal detection conditions. To tackle these limitations, we propose a novel reconstruction method based on physics-informed neural networks (PINNs).&#xD;&#xD;Approach:&#xD;The network model is optimized with constraints from observational data, wave equation, and initial conditions, to reconstruct the spatial distribution of initial acoustic pressure. The PINN framework further incorporates time reversal method to improve computational efficiency.&#xD;&#xD;Main results:&#xD;Comprehensive PAT simulations demonstrate that the PINN achieves superior reconstruction quality compared with conventional algorithms. The PINN exhibits favorable adaptivity to both heterogeneous and lossy media, and strong robustness against noisy measurements, sparse sensor layouts and limited-view detection geometries.&#xD;&#xD;Significance:&#xD;The PINN integrates the physical interpretability and the powerful capability of neural networks to represent continuous acoustic pressure fields. It eliminates the requirement for training datasets, offering broad application potential for PAT.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207639","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}