Physics in medicine and biology最新文献

{"title":"CAFDIM: A Group Convolution and Self-Attention Fusion-Based Dual-Domain Iterative Method for Sparse-View CT Reconstruction.","authors":"Jizhong Duan, Chenghong Sun, Haibo Tao, Zhenyu Huang, Yu Liu","doi":"10.1088/1361-6560/aea303","DOIUrl":"https://doi.org/10.1088/1361-6560/aea303","url":null,"abstract":"<p><strong>Objective: </strong>Sparse-view computed tomography (CT) reduces radiation dose and acquisition time by decreasing the number of projection views, but it also makes image reconstruction severely ill-posed, leading to structural distortion and severe artifacts. This study aims to develop an effective reconstruction framework for improving both projection-data fidelity and reconstructed image quality in sparse-view CT.</p><p><strong>Approach: </strong>We propose a group Convolution- and self-Attention Fusion-based Dual-domain Iterative Method (CAFDIM) for sparse-view CT reconstruction. CAFDIM follows a model-informed dual-domain iterative design. The framework consists of the Initialization Enhancement Network (IE-Net), Gradient Update Block (GUB), Projection-domain Repair Network (PR-Net), Image-domain Repair Network(IR-Net), and Momentum Update Block (MUB). The projection-domain branch employs a Deep Sparse Block (DSB) to enhance sparse projection features before full-view projection restoration, while the image-domain branch uses edge-guided residual refinement to improve anatomical structure preservation. To enhance local-global feature representation, a Convolution-Attention Fusion Block (CAFB) is embedded into both repair branches by combining group convolution with Pixel Shift Self-Attention (PSSA).</p><p><strong>Results: </strong>Experiments on simulated and real clinical projection datasets demonstrate that CAFDIM effectively suppresses sparse-view artifacts, preserves anatomical structures, and achieves superior reconstruction accuracy, visual quality, and generalization ability compared with state-of-the-art methods.</p><p><strong>Significance: </strong>CAFDIM provides an effective and efficient dual-domain reconstruction framework for sparse-view CT, showing strong potential for clinical applications in sparse-view and low-dose CT imaging.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892277","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":"Rapid whole-brain susceptibility source separation using sub-minute three-dimensional multiple overlapping-echo detachment acquisition.","authors":"Longkun Chen, Qinqin Yang, Shiwei Huang, Nuowei Ge, Zejun Wu, Jianjun Zhou, Jianfeng Bao, Lijun Bao, Yuanhao Cheng, Jianfa Chen, Shuhui Cai, Zhong Chen, Liuhong Zhu, Congbo Cai","doi":"10.1088/1361-6560/ae9d11","DOIUrl":"10.1088/1361-6560/ae9d11","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic susceptibility source separation provides profound insights into tissue pathophysiology by disentangling the underlying paramagnetic and diamagnetic sources within the human brain. However, the highly time-consuming nature of existing acquisition techniques creates a critical need for rapid and reliable separation methodologies to facilitate routine clinical applications.<i>Approach.</i>A highly accelerated framework for whole-brain magnetic susceptibility source separation was proposed by utilizing the ultra-fast 3D-MOLED sequence. This pipeline integrates the advanced deep learning architecture to accurately map the separated susceptibility components without relying on spin echo data. To ensure quantitative stability and structural fidelity, a robust three-dimensional domain adaptation strategy was incorporated during the simulation phase. The performance of the proposed method was systematically evaluated through phantom experiments, healthy volunteer assessments, and clinical examinations of patients with multiple sclerosis.<i>Main results.</i>The proposed framework achieved whole-brain susceptibility source separation at 1 mm isotropic resolution with only 50 s of data acquisition. In healthy volunteers, the paramagnetic and diamagnetic susceptibility maps were highly comparable to those obtained with the conventional multi-echo gradient-echo sequence, with structural similarity indices of 0.9244 ± 0.0181 and 0.9425 ± 0.0186, peak signal-to-noise ratios of 33.76 ± 2.37 and 37.29 ± 3.51 dB, regional coefficients of determination of 0.973 and 0.934, and Bland-Altman biases of -0.0013 and -0.0010 ppm, respectively. Furthermore, clinical evaluations in patients with multiple sclerosis demonstrated consistent characterization of complex lesion-related susceptibility features.<i>Significance.</i>Achieving whole-brain magnetic susceptibility source separation in less than one minute represents a meaningful technical advance. By substantially reducing the acquisition time required for susceptibility source separation, this accelerated framework may improve the feasibility of future clinical translation in neuroimaging workflows.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797011","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 physics-informed decomposition network for carbon ion radiotherapy dose monitoring: a proof-of-concept study.","authors":"Xin-Yu Hu, Yan Li, Wei-Guang Li, Yu-Ying Yin, Chao Yang, Cheng Chang, Ming-Qing Wang, Kai-Wen Li, Xueying Yang, Li-Sheng Geng","doi":"10.1088/1361-6560/ae9d0f","DOIUrl":"10.1088/1361-6560/ae9d0f","url":null,"abstract":"<p><p><i>Objective.</i>In-beam positron emission tomography (PET) provides a promising strategy for dose monitoring in carbon ion radiotherapy (CIRT), but accurate dose prediction remains difficult due to the complex, nonlinear relationship between positron-emitter activity and physical dose deposition. This proof-of-concept study aimed to improve activity-to-dose mapping by developing decomposition-based deep learning frameworks with auxiliary physical supervision.<i>Approach.</i>Idealized Monte Carlo (MC) simulations were conducted on computed tomography (CT) phantoms from 18 non-small cell lung cancer patients. The models were designed to predict laterally integrated one-dimensional depth-dose distributions for individual pencil-beam spots from corresponding 5 min cumulative activity and CT Hounsfield unit profiles. Two decomposition-based models, TemcoNet and NucoNet, incorporated Transformer-based decomposition modules supervised by cumulative post-irradiation activity at 10, 15, and 20 min and nuclide-specific yields of¹¹C,¹⁵O, and¹⁰C, respectively, while DirectNet served as a baseline.<i>Main results.</i>Compared with MC ground truth, all models achieved similar median range accuracy, but TemcoNet and NucoNet substantially improved dose prediction, reducing the mean relative error from 2.36% for DirectNet to below 0.4%. The mean gamma passing rate at 2 mm/2% increased from 45.31% to approximately 96% for both decomposition-based models. Ablation experiments showed that the decomposition pathway learned physically meaningful intermediate representations, and that nuclide-yield supervision provided an additional dose-prediction benefit.<i>Significance.</i>Physics-informed decomposition-based modeling improves MC-derived positron-emitter activity-to-dose mapping by combining effective representation learning with auxiliary physical supervision. The proposed framework improves dose prediction while incorporating physically meaningful priors into the learning process, offering a promising basis for PET-based dose-monitoring model development in CIRT.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148796962","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":"Diagnosis of clinically significant prostate cancer in multiparametric MRI with pseudo-localization of suspected lesion.","authors":"Xijun Liu, Rongzong Liu, Xin Zhou, Yifei Yan, Haihao He, Quan Zhou, Limin Zhang, Qi Zhang","doi":"10.1088/1361-6560/ae9e7f","DOIUrl":"10.1088/1361-6560/ae9e7f","url":null,"abstract":"<p><p><i>Objective.</i>To develop a two-stage diagnostic framework using pseudo-localization for patient-level diagnosis of clinically significant prostate cancer (csPCa) on multiparametric magnetic resonance imaging (mpMRI), enabling better utilization of examinations without manual lesion annotations.<i>Approach.</i>We included 1494 mpMRI examinations from 'the Prostate Imaging: Cancer AI' (PI-CAI) dataset, partitioned at the patient level into 217 annotated csPCa, 202 unannotated csPCa, and 1075 non-csPCa cases. First, a prostate transformer U-Net (PTUnet) was trained on the 217 annotated cases via five-fold cross-validation to generate pseudo-locations for the 202 unannotated csPCa cases. These pseudo-locations, along with expert lesion annotations and non-csPCa labels, were used to train an anisotropic UX-Net (AUX-Net). The probability-ushered lesion selection (PULSE) procedure derived patient-level diagnostic probabilities. We evaluated PTUnet, AUX-Net, PULSE, and the pseudo-localization strategy against representative models and baselines, and analyzed the link between pseudo-localization quality and diagnostic performance.<i>Main Results.</i>PTUnet achieved a dice similarity coefficient (DSC) of 64.96 ± 4.22% and an average precision of 65.69 ± 5.45%, the best overall pseudo-localization performance among evaluated models. All four diagnostic models showed higher area under the curves (AUC) with pseudo-locations. AUX-Net with PULSE achieved an AUC of 83.11%, sensitivity of 82.35%, specificity of 75.75%, and Youden's index of 58.10%, compared with the 80.96% AUC of AUX-Net without pseudo-locations. Pseudo-locations from nine different models all improved diagnostic AUC over the no-pseudo-location baseline, indicating the benefit is not model-specific. Furthermore, localization DSC was significantly and positively correlated with diagnostic AUC (Spearman's<i>ρ</i>= 0.800,<i>p</i>= 0.0096), suggesting higher-quality pseudo-localization leads to better diagnosis.<i>Significance.</i>The framework allows csPCa examinations without manual annotations to contribute to diagnostic model training. Results show pseudo-localization improves patient-level diagnosis across different architectures, and its quality is positively associated with downstream performance. The framework shows potential for non-invasive AI-assisted csPCa diagnosis and biopsy triage, pending further validation on independent larger-scale datasets and prospective clinical cohorts.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819265","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 real-time image-guided six-degree-of-freedom internal-external correlation framework for motion monitoring on a standard linear accelerator.","authors":"Alicja Kaczynska, Freeman Jin, Maegan Stewart, John Kipritidis, Jeremy Booth, Nicholas Hardcastle, Paul Keall, Chandrima Sengupta","doi":"10.1088/1361-6560/ae9e81","DOIUrl":"10.1088/1361-6560/ae9e81","url":null,"abstract":"<p><p><i>Objective.</i>Real-time six-degree-of-freedom (6DoF) tumour motion monitoring is important for accurate stereotactic body radiotherapy (SBRT) of thoracic and abdominal cancer sites, yet existing approaches either require specialised imaging hardware or continuous fluoroscopic imaging. This study presents the development and first experimental implementation of a real-time image-guided 6DoF internal-external correlation (6D-IEC) modelling framework on a standard linear accelerator.<i>Approach.</i>The 6D-IEC framework estimates 6DoF tumour motion using a state-augmented linear correlation model between a 1DoF external respiratory monitor and internal fiducial marker positions, updated with intrafraction kV images every 3 s. Performance was characterised through two complementary evaluations: a prospective experimental investigation on a robotic motion platform, providing independent ground truth across eight patient-measured lung and liver motion traces (3 regular, 5 irregular); and a patient data study using intrafraction imaging data from three patients treated in the TROG 17.03 LARK liver SBRT trial (NCT02984566). 6DoF geometric error was quantified for both evaluations; software processing latency was measured in the experimental investigation.<i>Main results.</i>In the experimental investigation, mean ± SD geometric errors were 0.1 ± 1.0 mm, 0.3 ± 1.9 mm, and -0.3 ± 1.4 mm along the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) translational axes, and 0.4°± 1.2°, -0.8°± 1.0°, and -0.2°± 0.8° around the corresponding rotational axes (rLR, rSI, rAP). Software processing latency was 190 ± 80 ms. In the patient data study, errors were -0.5 ± 0.7 mm, -1.2 ± 2.5 mm, -0.1 ± 1.0 mm (LR, SI, AP), and 1.1°± 1.7°, -0.9°± 1.5°, and -0.4°± 1.6° (rLR, rSI, rAP).<i>Significance.</i>This work demonstrates that real-time 6DoF motion monitoring with clinically relevant accuracy is achievable on a standard linear accelerator without specialised hardware or continuous kV imaging. 6D-IEC infers tumour motion from an external signal between imaging updates, with accuracy contingent on IEC, but requires approximately 95% fewer intrafraction kV images than continuous fluoroscopic approaches. This has implications for broader access to real-time image-guided radiotherapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819258","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":"S2V-DREME: a time-resolved slice-to-volume MR image reconstruction framework with dynamic reconstruction and motion estimation.","authors":"Xiaoxue Qian, Hua-Chieh Shao, Jie Deng, Yan Dai, You Zhang","doi":"10.1088/1361-6560/ae9d13","DOIUrl":"10.1088/1361-6560/ae9d13","url":null,"abstract":"<p><p><i>Objective.</i>Existing volumetric magnetic resonance imaging (MRI) techniques are constrained by the trade-off between acquisition time and image quality, limiting accuracy in motion-impacted sites such as the liver. To enable fast, better-quality volumetric imaging with sufficient spatiotemporal resolution, we developed a time-resolved volumetric MRI technique that recovers 3D volumes from acquired 2D MR slices for real-time 3D anatomy and motion tracking.<i>Approach.</i>2D MR slices dynamically acquired in time and space were mapped to time-resolved 3D MRIs using a one-shot slice-to-volume framework, S2V-DREME. The model jointly estimates a reference 3D MRI and time-resolved deformation vector fields (DVFs) that warp the reference volume into dynamic 3D MRIs. The reference volume is represented by a spatial implicit neural representation (INR), while the DVFs are derived via low-rank motion modeling. Motion basis components (MBCs) are generated by a spline-enhanced INR (SINR)-based motion generator, with coefficients inferred by a feature-wise linear modulation-based motion encoder. A progressive optimization strategy sequentially initializes the spatial INR and MBCs before joint optimization. The loss function integrates slice data fidelity, total variation regularization, MBC normalization, and DVF smoothness constraints.<i>Main results.</i>S2V-DREME generates time-resolved volumetric MRIs from 2D MR slice inputs. It was evaluated on digital phantom extended cardiac torso (XCAT), physical phantom, and human studies. In XCAT, it accurately captured regular and irregular motion during dynamic reconstruction (training stage, Dice similarity coefficient (DSC)/COME: 0.92 ± 0.03/0.98 ± 0.43 mm) and real-time motion estimation (testing stage, DSC/COME: 0.91 ± 0.02/0.99 ± 0.73 mm). Physical phantom experiments achieved a mean COME of 1.16 mm, and human studies demonstrated the feasibility of time-resolved 3D reconstruction from orthogonal-view and single-view slice acquisitions.<i>Significance.</i>By combining a novel step-and-shoot acquisition protocol with motion-compensated one-shot learning, S2V-DREME enables accurate time-resolved volumetric MRI reconstruction and motion tracking from cineslices, with strong potential for rapid volumetric imaging and real-time MR-guided adaptive radiotherapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13540005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797021","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":"Parameterizable salivary gland model for small-scale Monte Carlo radiopharmaceutical therapy dosimetry: evaluation of mouse and human models.","authors":"David P Adam, Tahir Yusufaly, Ian Marsh, Remco Bastiaannet, Ana Kiess, Wesley E Bolch, Nouran Zaid, Anupriya Chhabra, Rebecca Krimins, Kathy Gabrielson, Cory Brayton, George Sgouros, Robert F Hobbs","doi":"10.1088/1361-6560/ae9686","DOIUrl":"10.1088/1361-6560/ae9686","url":null,"abstract":"<p><p>Standard of care radiopharmaceutical therapy (RPT) may result in dose-limiting side effects of the salivary glands including sialadenitis and xerostomia that are inconsistent with outcomes from external beam for equivalent absorbed doses (ADs). This work develops a parameterizable 'Macro-to-Micro' (M2<i>μ</i>) model that demonstrates the consideration of small-scale dosimetry in comparison to conventional methods that typically assume uniform voxel or organ-level uptake. Anatomical features of the salivary gland are represented by annular structures (a centralized branching network of ducts, including excretory, lobar, interlobular) and small-scale voxels (intralobular ducts and acinar cells) with dimensions set to reproduce<i>ex vivo</i>murine histopathology measurements and scaled up in size for extrapolation to humans. Simulations were performed by scoring and recording<i>S</i>-value histograms to the target ductal and acinar cells, for two beta emitters (¹⁷⁷Lu,¹³¹I) and one alpha emitter (²²⁵Ac). Four idealized activity distributions were created to assign activity to surfaces and volumes of the annuli, and GEANT4 v11 was used for radiation transport calculations. Comparisons against whole gland uniform spherical self-dose<i>S</i>-value calculations were conducted to validate the radiation transport and to highlight differences between M2<i>μ</i>and conventional dosimetry approaches. Analyses for both models showed greater<i>S</i>-value variation in comparison to the homogeneously distributed activity<i>S</i>-value calculation. The most notable result was that the calculated<i>S</i>-values differed between different branches, depending on the geometric size of the annuli. For the surrounding acinar cells,<i>S</i>-values from ductal cells decreased as a function of distance from the branching structures. High variability of<i>S</i>-values, depending on ductal cell dimensions as well as within the acinar cell distribution of the salivary gland highlights the potential clinical utility of small-scale approaches to RPT salivary gland dosimetry, contingent on clinical translation and validation. To this end, future work should further refine the model and incorporate small-scale activity distributions from pre-clinical and translational studies.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148685392","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":"Evaluation of volumetric breathing motion prediction of the stomach using dynamic golden-angle radial MRI.","authors":"Robert Jones, Lianli Liu, Daekeun You, Jeffrey A Fessler, James M Balter","doi":"10.1088/1361-6560/ae9117","DOIUrl":"10.1088/1361-6560/ae9117","url":null,"abstract":"<p><p><i>Objective</i>. The motion of abdominal organs complicates accurate radiotherapy (RT) planning and delivery. Accurate short-term prediction of respiratory motion is essential for image-guided adaptive RT in the abdomen. This study evaluates the performance of a kernel ridge regression-based breathing motion prediction framework (KRR-BMP) for abdominal organs, with particular emphasis on the stomach and its centerline.<i>Approach</i>. Dynamic MRI datasets from 27 scans acquired in 16 patients were processed to generate low-rank breathing motion models based on principal component analysis (PCA) of deformation vector fields (DVFs). KRR-BMP was used to predict future PCA coefficients of breathing motion, enabling reconstruction of volumetric breathing motion deformation fields at specified prediction horizons. The accuracy of predicted breathing DVFs was evaluated across multiple organs and anatomical structures, training set sizes, PCA ranks, and prediction horizons.<i>Main results</i>. At a 340 ms prediction horizon, KRR-BMP achieved sub-voxel accuracy across all evaluated organs, with population-mean endpoint errors below 1.3 mm and 95th percentile errors consistently below 3 mm. Error distributions were relatively narrow across subjects and training sets, demonstrating robustness and generalizability. Reductions in training data led to predictable increases in error, particularly for gastric structures, although population-level upper bound errors remained within clinically relevant ranges. A PCA rank of two provided the best balance between accuracy and robustness, while higher ranks increased inter-subject variability.<i>Significance</i>. KRR-BMP provides accurate and robust short-term respiratory motion prediction for abdominal organs in MR-guided RT. The demonstrated performance for stomach subregions establishes a foundation for future integration of respiratory motion prediction with real-time gastric motility modeling and motion-aware adaptive treatment workflows.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148606423","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 proton arc therapy sequencing optimization algorithm for brain stereotactic radiosurgery.","authors":"Peilin Liu, Lewei Zhao, Gang Liu, Xiaoda Cong, Zerun Zhang, Xiaoqiang Li, Xuanfeng Ding","doi":"10.1088/1361-6560/aea1bb","DOIUrl":"https://doi.org/10.1088/1361-6560/aea1bb","url":null,"abstract":"<p><strong>Objective: </strong>Proton arc (PAT) therapy combines the dosimetric advantages of protons with the efficiency of arc delivery, but current planning algorithms are based on a static delivery sequence. The discrepancy between the static plan and the actual delivery can result in dosimetric deviations during stereotactic radiosurgery (SRS), where precision is critical.&#xD;&#xD;Approach: A dynamic arc delivery sequencing optimization framework was developed, consisting of three steps: (1) static irradiation and dynamic arc delivery time calculation, (2) incorporation of timing information into static control points, and (3) spot-weighting fine-tuning. Eight multi-metastatic brain cases were retrospectively selected to validate the framework. Plan quality, delivery accuracy, and efficiency were evaluated by reconstructing the delivered dose from virtual logfiles and comparing dosimetric parameters and treatment times.&#xD;&#xD;Main results: Sequencing optimization maintained nominal plan quality, with no significant differences in target coverage (D98) or normal brain sparing (V12, V8) compared with static-control-point PAT. Delivery accuracy improved substantially. For the total gross tumor volume, mean absolute D98 deviation decreased from 62.9 ± 70.1 cGyE (3.4% ± 3.8%) with static-control-point PAT to 11.0 ± 7.4 cGyE (0.6% ± 0.6%) with sequencing optimization. For the worst metastasis, deviations were reduced from 116.4 ± 89.1 cGyE (6.3% ± 5.3%) to 79.4 ± 69.2 cGyE (3.6% ± 3.0%). Delivery efficiency was preserved, with minimal changes in spot number, energy layers, and total treatment time.&#xD;&#xD;Significance: Dynamic sequencing optimization significantly improves the dosimetric fidelity of PAT therapy for brain SRS while maintaining efficiency. By addressing machine-specific timing and mechanical constraints, this framework bridges the gap between nominal planning and clinical delivery, representing an essential step toward routine PAT implementation.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880354","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":"Hamiltonian dynamics for stochastic reconstruction in emission tomography.","authors":"Theodoros Leontiou, Anna Frixou, Elena Ttofi, Charalambos Chrysostomou, Yiannis Parpottas, Konstantinos Michael, Savvas Frangos, Efstathios Stiliaris, Costas N Papanicolas","doi":"10.1088/1361-6560/aea1d7","DOIUrl":"https://doi.org/10.1088/1361-6560/aea1d7","url":null,"abstract":"<p><p><i>Objective.</i>To develop a practical stochastic reconstruction framework for emission&#xD;tomography that generates ensembles of data-compatible images and enables&#xD;uncertainty quantification and assessment of forward-model adequacy.<i>Approach.</i>&#xD;The framework combines stochastic-gradient descent initialization with&#xD;Hamiltonian Monte Carlo (HMC) sampling directly in high-dimensional voxel&#xD;space. Beyond point reconstruction, we introduce a spatially resolved&#xD;operator-weighted diagnostic, the sampled data-visible variance, which&#xD;quantifies how image fluctuations propagate through the imaging operator&#xD;and thereby probes the local conditioning of the inverse problem under&#xD;realistic acquisition physics. The methodology is evaluated using&#xD;controlled software phantoms, experimental anthropomorphic phantom&#xD;measurements, and a clinical DATSCAN SPECT acquisition.<i>Main results.</i>&#xD;Under ideal conditions, the HMC ensemble mean provides point-estimate&#xD;accuracy comparable to deterministic reconstruction methods, while the&#xD;sampled ensemble provides additional physically interpretable information.&#xD;The ensemble analysis helps distinguish uncertainty associated with the&#xD;intrinsic ill-posedness of the inverse problem from variability linked to&#xD;forward-model inadequacy. The clinical example demonstrates applicability&#xD;under realistic acquisition statistics rather than diagnostic performance.&#xD;<i>Significance.</i>The proposed stochastic reconstruction framework provides a practical&#xD;ensemble-based approach for emission tomography that extends conventional&#xD;point reconstruction with model-conditioned uncertainty estimates and&#xD;spatially resolved diagnostics of forward-model adequacy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880942","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}