Physics in medicine and biology最新文献

{"title":"Beam orientation optimization in IMRT using sparse mixed integer programming and non-convex fluence map optimization.","authors":"Yang Lei, Jiahan Zhang, Kaida Yang, Shouyi Wei, Ruirui Liu, Yabo Fu, Yu Lei, Haibo Lin, Charles B Simone, Kenneth Rosenzweig, Tian Liu","doi":"10.1088/1361-6560/ade8ce","DOIUrl":"10.1088/1361-6560/ade8ce","url":null,"abstract":"<p><p><i>Objective.</i>Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, non-convex problem traditionally addressed with heuristic methods.<i>Approach.</i>This work demonstrates the potential improvement of the proposed BOO, providing a mathematically grounded benchmark that can guide and validate heuristic BOO methods, while also offering a computationally efficient workflow suitable for clinical application. A novel framework integrating second-order cone programming (SOCP) relaxation, sparse mixed integer programming (SMIP), and deep inverse optimization is proposed. Nonconvex dose-volume constraints were managed via SOCP relaxation, ensuring convexity while maintaining sparsity. BOO was formulated as an SMIP problem with binary beam selection, solved using an augmented Lagrange method. To accelerate optimization, a neural network approximated optimal solution, improving computational efficiency eightfold. A retrospective analysis of 12 locally advanced non-small cell lung cancer (NSCLC) patients (60 Gy prescription) compared automated BOO-selected beam angles with expert selections, evaluating dosimetric metrics such as planning target volume (PTV) maximum dose, D98%, lung V20, and mean heart and esophagus dose.<i>Main results.</i>In 12 retrospective study, the automated BOO demonstrated superior dose conformity and sparing of critical structures. Specifically, the BOO plans achieved comparable PTV coverage (maximum: 61.7 ± 1.4 Gy vs. 62.1 ± 1.5 Gy, D98%: 59.5 ± 0.7 Gy vs. 59.5 ± 0.6 Gy, D2%: 61.2 ± 1.3 Gy vs. 61.4 ± 1.4 Gy with<i>p</i>-values >0.5) but demonstrated improved sparing for lungs (V20: 9.8 ± 2.2% vs. 11.5 ± 2.3%,<i>p</i>-value: 0.01), heart (mean: 3.3 ± 0.6 Gy vs. 4.3 ± 0.5 Gy,<i>p</i>-value: 0.04), esophagus (mean: 0.5 ± 1.3 Gy vs. 1.8 ± 1.5 Gy,<i>p</i>-value: 0.02), and spinal cord (max: 7.2 ± 3.4 Gy vs. 9.0 ± 3.2 Gy,<i>p</i>-value < 0.01) compared to human-selected plans.<i>Significance.</i>This approach highlighted the potential of BOO to enhance treatment efficacy by optimizing beam angles more effectively than manual selection. This framework establishes a benchmark for BOO in IMRT, enhancing heuristic methods through a hybrid framework that combines mathematical optimization with targeted heuristics to improve solution quality and computational efficiency. The integration of SMIP and deep inverse optimization significantly improves computational efficiency and plan quality.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144507467","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":"Patient-specific virtual surgical planning for tongue reconstruction: evaluating hyperelastic inverse FEM with four simulated tongue cancer cases.","authors":"Amir Reza Isazadeh, Julianna Zenke, Lindsey Westover, Hadi Seikaly, Daniel Aalto","doi":"10.1088/1361-6560/adebd8","DOIUrl":"https://doi.org/10.1088/1361-6560/adebd8","url":null,"abstract":"<p><strong>Objective: </strong>Anatomically and functionally optimal tongue reconstruction after tumor removal presents significant challenges. Current Virtual Surgical Planning (VSP) utilizes patient-specific data with geometric algorithms for free flap design. However, these geometric approaches often inadequately account for complex soft tissue biomechanics. This study introduces a biomechanics-informed VSP algorithm and computationally compares its flap designs against those derived from purely geometric methods.</p><p><strong>Approach: </strong>Hyperelastic inverse Finite Element Method (hiFEM) was developed by integrating an Ogden hyperelastic constitutive model into a predecessor algorithm. The planar flap shape is determined by minimizing potential energy when tissue deforms to match patient-specific MRI-derived 3D defect geometry. Four clinically plausible tongue cancer cases were simulated, and resection regions delineated. For each case, flap designs were generated using hiFEM, its predecessor iFEM, and two geometric flattening techniques: NURBS surface flattening and Boundary First Flattening (BFF). Intrinsic tissue deformation for these designs was compared across methods and quantified using area stretch metric.</p><p><strong>Main results: </strong>Across all simulated cases, hiFEM-generated flap designs required less intrinsic tissue deformation. Maximum area stretch ranged from 1.10-1.12 for hiFEM designs, versus 1.19-1.38 for NURBS flattening and 1.54-1.74 for BFF designs. Furthermore, hiFEM's area stretch distribution was tighter, centered around one (ideal, no stretch). Geometric comparison showed hiFEM yields flap designs similar to the clinically validated geometric algorithm, NURBS flattening, with an average Hausdorff distance of only 1.3 mm. hiFEM's distinct advantage is its core objective of minimizing tissue stretch, which has clinical relevance and suggests potential for improved patient outcomes. Computationally, hiFEM demonstrated robustness and efficiency. It converged rapidly (8 to 10 iterations; less than 0.3s/case), even for complex geometries where iFEM failed.</p><p><strong>Significance: </strong>hiFEM offers a biomechanically informed and computationally robust tool for tongue VSP, showing potential for broader application in breast, nasal, and other soft tissue reconstructions.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560809","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":"Monte Carlo-based dosimetry and optimization of a custom alpha cell irradiation setup.","authors":"Maryam Rahbaran, Joanna Li, Shirin A Enger","doi":"10.1088/1361-6560/ade846","DOIUrl":"10.1088/1361-6560/ade846","url":null,"abstract":"<p><p><i>Objective.</i>When combined with targeting agents,<i>α</i>-particle-emitting radionuclides show promise in treating hypoxic tumors and micrometastases. These radionuclides exhibit a high relative biological effectiveness (RBE), attributed to their high linear energy transfer, and induce complex DNA damage within targeted cells. However, most clinical experience and radiobiological data are derived from photon irradiation. To optimize<i>α</i>-particle-based treatments, further research is needed to refine their RBE estimates. This study aimed to characterize and optimize a custom<i>in-vitro</i>cell irradiation setup for<i>α</i>-particle RBE studies using²⁴¹Am through Monte Carlo simulations.<i>Approach.</i>A Geant4-based Monte Carlo simulation model was used to simulate a custom cell well setup. An²⁴¹Am (48 kBq) source was positioned beneath the well with an adjustable source-to-surface distance (SSD). The spectra of decay products was calculated with 6.5×109simulated²⁴¹Am decay events. Simulations were conducted for SSD values of 2 mm, 5 mm, and 7 mm under three scenarios: (A) total dose rate from all decay products, (B) excluding<i>γ</i>-emissions, and (C) excluding secondary particles. Results were compared to published spectra and a published dose rate (0.1 Gy min^-1) as validation.<i>Main results.</i>The validation dose rate was 0.1136 Gy min^-1. Photons of 13.9-59.5 keV and<i>α</i>-particles of 5.39-5.48 MeV were observed. The dose inhomogeneity across the cells was around 30%, 10%, and 5% in the 2, 5, and 7 mm SSD setups, respectively. The corresponding total dose rates in cells for the three SSDs were 0.583, 0.146, and 0.0830 Gy min^-1. The dose rate contributions were 90% from<i>α</i>-particles, less than 0.07% from<i>γ</i>-emissions, and 9%-10% from secondary particles.<i>Significance.</i>To accurately assess radiobiological effects, it is important to consider the full decay spectrum of radionuclides and their secondary particles in dosimetry calculations. These findings will aid in refining experimental setups for future<i>in-vitro</i>studies, contributing to more reliable RBE calculations.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144497653","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":"ComptoNet: a Compton-map guided deep learning framework for multi-scatter estimation in multi-source stationary CT.","authors":"Yingxian Xia, Li Zhang, Yuxiang Xing, Zhiqiang Chen, Hewei Gao","doi":"10.1088/1361-6560/adebd7","DOIUrl":"https://doi.org/10.1088/1361-6560/adebd7","url":null,"abstract":"<p><p>Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantryless scan architecture and capability of simultaneous multi-source emission. However, the lack of anti-scatter grid deployment in MSS-CT leads to severe forward and cross scatter contamination, necessitating accurate and efficient scatter correction. In this work, we propose ComptoNet, an innovative decoupled deep learning framework that integrates Compton-scattering physics with deep learning for scatter estimation in MSS-CT. The core innovation lies in the Compton-map, a representation of large-angle Compton scatter signals outside the scan field of view. ComptoNet employs a dual-network architecture: a Conditional Encoder-Decoder Network (CED-Net) guided by reference Compton-maps and spare detector data for cross scatter estimation, and a Frequency U-Net with attention mechanisms for forward scatter correction. Experiments on Monte Carlo-simulated data demonstrate ComptoNet's superior performance, achieving a mean absolute percentage error (MAPE) of $0.84%$ on scatter estimation. After correction, CT images show nearly artifact-free quality, validating ComptoNet's robustness in mitigating scatter-induced errors across diverse photon counts and phantoms.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560807","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":"Performance study of monolithic crystal detector based on neighboring crystal light sharing with dual-ended readout.","authors":"Zhuoran Wang, Xianchao Huang, Daowu Li, Wei Zhou, Yingjie Wang, Xiangtao Zeng, Zexin Zhang, Yushuang Zheng, Weiyan Pan, Meiling Zhu, Qing Wei, Hang Yuan, Xiaoxuan Li, Zhiming Zhang, Long Wei","doi":"10.1088/1361-6560/adebd9","DOIUrl":"https://doi.org/10.1088/1361-6560/adebd9","url":null,"abstract":"<p><p><i>Objective.</i>The monolithic crystal detector has attracted attention due to its high detection efficiency and intrinsic depth of interaction (DOI) resolution. However, the inherent edge effects degrade the performance at the boundaries of the detector, resulting in reduced positioning accuracy. To address this issue, this paper proposes a dual-ended readout positron emission tomography (PET) detector based on neighboring monolithic crystal light sharing. By combining the advantages of light sharing and dual-ended readout, the spatial resolution at the edges of the monolithic crystal detector is improved.<i>Approach.</i>Four LYSO crystals measuring 40∗40∗20 mm³were utilized in this paper. A high-refractive-index optical adhesive was employed to bond the polished sides of the four crystals into a cohesive unit. In the two-dimensional plane positioning of the detector, the centre of gravity (COG) method is employed using a threshold reduction and a combined response calculation of the neighboring crystal light sharing.<i>Main results.</i>The results indicate that the dual-ended readout detector based on light sharing achieves spatial resolution results in the light-sharing area that are comparable to those in the central area, yielding a spatial resolution of 0.76 mm on the X=0 plane of a 20 mm thick crystal detector, significantly mitigating the edge effects of the monolithic crystal detector. Furthermore, the single-ended readout detector utilizing the light sharing technique has achieved a spatial resolution of 0.99 mm across the entire detector plane.<i>Significance.</i>The dual-ended readout detector utilizing light sharing among neighboring crystals effectively addresses the edge effect issue encountered in monolithic crystal detectors, thereby achieving high spatial resolution in thick crystals. This innovative approach presents an advantageous detector scheme for the advancement of high-sensitivity, high-spatial-resolution small animal PET.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560810","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":"Contrast-enhanced image synthesis using latent diffusion model for precise online tumor delineation in MRI-guided adaptive radiotherapy for brain metastases.","authors":"Xiangyu Ma, Yuchao Ma, Yu Wang, Canjun Li, Yuxiang Liu, Xinyuan Chen, Jianrong Dai, Nan Bi, Kuo Men","doi":"10.1088/1361-6560/ade845","DOIUrl":"10.1088/1361-6560/ade845","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) is a promising technique for long-course radiotherapy of large-volume brain metastasis (BM), due to the capacity to track tumor changes throughout treatment course. Contrast-enhanced T1-weighted (T1CE) MRI is essential for BM delineation, yet is often unavailable during online treatment concerning the requirement of contrast agent injection. This study aims to develop a synthetic T1CE (sT1CE) generation method to facilitate accurate online adaptive BM delineation.<i>Approach.</i>We developed a novel ControlNet-coupled latent diffusion model (CTN-LDM) combined with a personalized transfer learning strategy and a denoising diffusion implicit model inversion method to generate high quality sT1CE images from online T2-weighted (T2) or fluid attenuated inversion recovery (FLAIR) images. Visual quality of sT1CE images generated by the CTN-LDM was compared with other deep learning models. BM delineation results using the combination of our sT1CE images and online T2/FLAIR images were compared with the results solely using online T2/FLAIR images, which is the current clinical method.<i>Main results.</i>Visual quality of sT1CE images from our CTN-LDM was superior to competing models both quantitatively and qualitatively. Leveraging sT1CE images, radiation oncologists achieved significant higher precision of adaptive BM delineation, with average Dice similarity coefficient of 0.93 ± 0.02 vs. 0.86 ± 0.04 (<i>P <</i>0.01), compared with only using online T2/FLAIR images.<i>Significance.</i>The proposed method could generate high quality sT1CE images and significantly improve accuracy of online adaptive tumor delineation for long-course MRIgART of large-volume BM, potentially enhancing treatment outcomes and minimizing toxicity.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144497649","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":"Self-supervised learning for low-dose CT image denoising method based on guided image filtering.","authors":"Yu He, Xinwei Luo, Chengxiang Wang, Wei Yu","doi":"10.1088/1361-6560/ade847","DOIUrl":"10.1088/1361-6560/ade847","url":null,"abstract":"<p><p><i>Objective.</i>low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposure. Most existing deep learning-based denoising methods require supervised learning with paired training data that is difficult to obtain. To address this limitation, we aim to develop a denoising method that does not rely on paired normal-dose computed tomography data.<i>Approach.</i>we propose a self-supervised denoising method based on guided image filtering (GIF) that requires only LDCT images for training. The method first applies GIF to generate pseudo-labels from LDCT images, enabling the network to learn noise distributions between inputs and pseudo-labels for denoising, without paired data. Then, an attention gate (AG) mechanism is embedded in the decoder stage of a residual network to further enhance denoising performance.<i>Main results.</i>experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art unsupervised denoising networks, transformer-based denoising model and post-processing methods, in terms of both visual quality and quantitative metrics. Furthermore, ablation studies are conducted to analyze the impact of different attention mechanisms and the number of AG mechanisms, showing that the proposed network architecture achieves optimal performance.<i>Significance.</i>this work leverages self-supervised learning with GIF to generate pseudo-labels, enabling LDCT denoising without paired data. The embedded AG mechanism, supported by detailed ablation analysis, further enhances denoising performance by improving feature focus and structural preservation.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144497656","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":"Optimising proton stopping power ratio prediction with spectral cone-beam CT.","authors":"David Leibold, Dennis R Schaart, Marlies C Goorden","doi":"10.1088/1361-6560/adebd6","DOIUrl":"https://doi.org/10.1088/1361-6560/adebd6","url":null,"abstract":"<p><strong>Objective: </strong>Cone-beam computed tomography (CBCT) is used for patient positioning in proton therapy, but not directly for treatment planning due to its inferior image quality compared to fan-beam CT. One way to improve its value for proton radiotherapy might be to use CBCT setups capable of extracting spectral information, which can be realised through several hardware configurations. Here, we compare different setups w.r.t. to their capability of predicting proton stopping power ratios (SPR).&#xD;Approach: We investigate six different spectral CBCT realisations in a simulation study, namely a single-source setup with either a dual-layer detector or a photon-counting detector (PCD), a kVp-switching setup with either an energy-integrating detector (EID) or a PCD, and a dual-source setup with either EIDs or PCDs. Our figure of merit is the normalised Cramér-Rao Lower Bound (nCRLB) on SPR variance based on projection data. We take (cross)scatter into account, and compare ideal and realistic detector models to help guide future detector developments. Each setup is optimised w.r.t. source spectra, mAs ratios and energy bin settings (where applicable).&#xD;Main results: Assuming a realistic detector response, setups with a kVp-switching source perform best, with the setup paired with an EID slightly outperforming the PCD-based setup (nCRLBs of 2.70 and 2.82, respectively). However, if the mAs ratio of the kVp-switching source is fixed, the performance of the kVp-switching setup with an EID is significantly degraded (nCRLB = 9.04) and outperformed by PCD-based setups, with nCRLBs of 3.62, 3.80 and 3.98 for the dual-source setup with two PCDs, the kVp-switching setup and the single-source setup with one PCD, respectively. Spectra with higher mean energy or wider spectral separation generally yield lower CRLB values, and avoiding the spectral distortion caused by charge sharing in direct-conversion PCDs promises to lower CRLB values by about a third.&#xD;Significance: We present an extensive comparison of spectral CBCT setups for their application in proton radiotherapy, using a methodology that allows to compare their theoretical limit of performance without being influenced by the choice of reconstruction algorithm or the conversion scheme from Hounsfield units to SPR values.&#xD.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560808","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":"optiGAN: a deep learning-based alternative to optical photon tracking in Python-based GATE (10+).","authors":"Guneet Mummaneni, Carlotta Trigila, Nils Krah, David Sarrut, Emilie Roncali","doi":"10.1088/1361-6560/ade2b5","DOIUrl":"10.1088/1361-6560/ade2b5","url":null,"abstract":"<p><p><i>Objective.</i>To accelerate optical photon transport simulations in the GATE medical physics framework using a generative adversarial network (GAN), while ensuring high modeling accuracy. Traditionally, detailed optical Monte Carlo methods have been the gold standard for modeling photon interactions in detectors, but their high computational cost remains a challenge. This study explores the integration of optiGAN, a GAN model into GATE 10, the new Python-based version of the GATE medical physics simulation framework released in November 2024.<i>Approach.</i>The goal of optiGAN is to accelerate optical photon transport simulations while maintaining modeling accuracy. The optiGAN model, based on a GAN architecture, was integrated into GATE 10 as a computationally efficient alternative to traditional optical Monte Carlo simulations. To ensure consistency, optical photon transport modules were implemented in GATE 10 and validated against GATE v9.3 under identical simulation conditions. Subsequently, simulations using full Monte Carlo tracking in GATE 10 were compared to those using GATE 10-optiGAN.<i>Main results.</i>Validation studies confirmed that GATE 10 produces results consistent with GATE v9.3. Simulations using GATE 10-optiGAN showed over 92% similarity to Monte Carlo-based GATE 10 results, based on the Jensen-Shannon distance across multiple photon transport parameters. optiGAN successfully captured multimodal distributions of photon position, direction, and energy at the photodetector face. Simulation time analysis revealed a reduction of approximately 50% in execution time with GATE 10-optiGAN compared to full Monte Carlo simulations.<i>Significance.</i>The study confirms both the fidelity of optical photon transport modeling in GATE 10 and the effective integration of deep learning-based acceleration through optiGAN. This advancement enables large-scale, high-fidelity optical simulations with significantly reduced computational cost, supporting broader applications in medical imaging and detector design.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258717","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":"Microdosimetric evaluation of a clinical carbon ion beam using a tissue-equivalent proportional counter.","authors":"Shannon Hartzell, Phillip J Taddei, Fada Guan, Alfredo Mirandola, Paige Taylor, Mario Ciocca, Giuseppe Magro, Christine B Peterson, Stephen F Kry","doi":"10.1088/1361-6560/adeb3e","DOIUrl":"https://doi.org/10.1088/1361-6560/adeb3e","url":null,"abstract":"<p><p>Microdosimetry provides critical insights into radiation quality and relative biological effectiveness (RBE) in carbon ion therapy. However, its application in modern pulsed scanning beams is limited due to detector response challenges at high dose rates. This study evaluates the feasibility of using a commercially available spherical tissue-equivalent proportional counter (TEPC) (LET-1/2, Far West Technologies) to measure microdosimetric spectra in a clinical carbon ion beam, comparing results with Monte Carlo simulations.&#xD;Microdosimetric measurements were performed at the National Center for Oncological Hadrontherapy (CNAO) using the LET-1/2 filled with 33.1 mmHg tissue-equivalent gas to simulate a 1 µm diameter tissue volume. Measurements were conducted at various depths and beam energies, covering a range of LETs. Data were processed to obtain frequency- and dose-weighted lineal energy distributions, from which saturation-corrected dose-mean lineal energy values and RBE calculated with the modified microdosimetric kinetic model were derived. Monte Carlo simulations using TOPAS replicated the experimental setup for comparison.&#xD;TEPC measurements demonstrated reasonable agreement with Monte Carlo simulations when the beam intensity was sufficiently reduced to mitigate pulse pileup. At the standard clinical beam intensity, microdosimetric spectra exhibited distortions due to pulse pileup effects, leading to overestimation of high lineal energy events. However, when the lowest-intensity synchrotron mode was used to achieve reduced beam intensities (~1.5×10⁵ particles/s), agreement between measured and simulated spectra improved significantly, with RBE values derived from the measured spectra agreeing with Monte Carlo predictions to <4%. Background subtraction techniques and repeatability analyses confirmed the robustness of the TEPC measurements under optimized conditions.&#xD;This study establishes conditions under which a TEPC can reliably measure microdosimetric spectra in pulsed carbon ion beams. These findings support its potential for clinical applications, including treatment verification and quality assurance in carbon ion therapy. The results contribute to ongoing efforts in RBE assessment and credentialing for clinical trials.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144554196","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}