Physics in medicine and biology最新文献

{"title":"Dose adaptation to compensate for cumulative intra-fraction motion effects in online adaptive radiotherapy.","authors":"Erik van der Bijl, Robert Jan Smeenk, Lukas Schröder, Jan-Jakob Sonke, Uulke A van der Heide, Tomas Janssen","doi":"10.1088/1361-6560/add984","DOIUrl":"10.1088/1361-6560/add984","url":null,"abstract":"<p><p><i>Objective.</i>The objective of this work was to investigate the feasibility of using 0 mm PTV margin in online adaptive radiotherapy for the first fractions, in combination with treatment-specific local compensation of accumulated underdosage to the target in the last fraction.<i>Approach.</i>Intrafraction motion patterns and delineations of twelve patients with prostate cancer were selected to cover a range of observed systematic and random inter- and intrafraction motion patterns. Treatment plans with 0 and 3 mm margins were created and dose was accumulated rigidly using the observed motion patterns. For the dose-adaptation approach a plan was created for the last treatment fraction locally compensating for dose missed in the previous fractions. Robustness of the accumulation was estimated by simulating treatments with random registration errors added to the observed registrations, with standard deviations of 0.5 and 1.0 mm.<i>Main results.</i>Target coverage of the dose-adaptive workflow was not-significantly below the standard approach, and at the desired level but for the two patients with the largest systematic prostate motion. The near-maximum dose to the organs at risk is lowered for all patients with a median of 1.5 Gy. The total volume receiving 95% of the prescribed dose was reduced by 15% to 1.6 times the clinical target volume indicating better conformity, at the cost of an increased near-maximum dose to the target. However, the dose-adaptive plan was less robust leading to a median 0.5% decrease in dose to the target also with decreasing robustness with larger motion patterns.<i>Significance.</i>The results demonstrate that a post-hoc correction of missed dose leads to an overall lower dose to nearby organs at risk at the cost of target dose near-maximum dose, making it a feasible approach for consideration.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079572","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":"Exploring the redundancy of Radon transform using a set of partial derivative equations: could we precisely reconstruct the image from a sparse-view projection without any image prior?","authors":"Xuanqin Mou, Jiayu Duan","doi":"10.1088/1361-6560/add839","DOIUrl":"10.1088/1361-6560/add839","url":null,"abstract":"<p><p>In this study, we propose a universal<i>n</i>th order partial differential equation (PDE) of 2D Radon transform to disclose the correlation of Radon transform among neighboring integration line. Specifically, a CT geometry of dual centers of rotation is introduced to formulate an object independent PDE that presents the local correlation of Radon transform on the variables of distance and angle, named LCE (local correlation equation). The LCE is directly available to divergent beam CT geometries, e.g. fan beam CT or cone beam CT. In this case, one rotation center is set at the focal spot, so that the LCE becomes a general PDE for actually used CT systems with single rotation center (origin). Thus, we deduce two equivalent LCE forms for two widely used CT geometries, i.e. cLCE for circular scanning trajectory and sLCE for stationary linear array scanning trajectory, respectively. The LCE also explores the redundancy property existed in Radon transform. One usage of the LCE is that it supports a sparse-view projection could contain enough information of complete projection, and hence projection completeness in CT scanning would be no longer needed. In this regard, based on the circular scanning trajectory, we explore whether the cLCE is able to solve sparse-view problem without the help of image prior. We propose a discrete cLCE based interpolation scheme that can be solved by a matrix inversion based on Lagrange multiplier method. The analysis on the matrix inversion shows that the interpolation matrix is full rank although the condition number of the matrix is larger when the sparsity increases. The fact suggests that sparse-view CT projection indeed contains enough information of complete projection, which is independent of the scanned object. Moreover, a unified reconstruction framework combining a regularized iterative reconstruction with the cLCE based interpolation is also proposed to cope with higher sparsity level. In experimental validation, we chose 1/4 and 1/8 sparsity to verify the discrete cLCE interpolation method and the unified reconstruction scheme, respectively. The results confirm that the sparse-view projection is feasible to realize a comparable reconstruction as from complete projection based on the LCE. It would be expected that combining the LCE property will boost various researches on CT reconstructions in the future.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974935","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":"Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT.","authors":"Brayden Schott, Victor Santoro-Fernandes, Žan Klaneček, Scott Perlman, Robert Jeraj","doi":"10.1088/1361-6560/add9df","DOIUrl":"10.1088/1361-6560/add9df","url":null,"abstract":"<p><p><i>Objective.</i>Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncertainty quantification (UQ) methods exist to capture model uncertainty. Yet, it is not clear which method is optimal for a given task. The purpose of this work was to investigate several commonly used UQ methods for the critical yet understudied task of metastatic lesion segmentation on whole body PET/CT.<i>Approach.</i>59 whole body⁶⁸Ga-DOTATATE PET/CT images of patients undergoing theranostic treatment of metastatic neuroendocrine tumors were used in this work. A 3D U-Net was trained for lesion segmentation following five-fold cross validation. Uncertainty measures derived from four UQ methods-probability entropy, Monte Carlo dropout, deep ensembles, and test time augmentation-were investigated. Each uncertainty measure was assessed across four quantitative evaluations: (1) its ability to detect artificially degraded image data at low, medium, and high degradation magnitudes; (2) to detect false-positive (FP) predicted regions; (3) to recover false-negative (FN) predicted regions; and (4) to establish correlations with model biomarker extraction and segmentation performance metrics.<i>Main</i><i>results.</i>Test time augmentation and probability entropy respectively achieved the highest and lowest degraded image detection at low (AUC = 0.54 vs. 0.68), medium (AUC = 0.70 vs. 0.82), and high (AUC = 0.83 vs. 0.90) degradation magnitudes. For detecting FPs, all UQ methods achieve strong performance, with AUC values ranging narrowly between 0.77 and 0.81. FN region recovery performance was strongest for test time augmentation and weakest for probability entropy. Performance for the correlation analysis was mixed, where the strongest performance was achieved by test time augmentation for SUV_totalcapture (ρ= 0.57) and segmentation Dice coefficient (ρ= 0.72), by Monte Carlo dropout for SUV_meancapture (ρ= 0.35), and by probability entropy for segmentation cross entropy (ρ= 0.96).<i>Significance.</i>Overall, test time augmentation demonstrated superior UQ performance and is recommended for use in metastatic lesion segmentation task. It also offers the advantage of being post hoc and computationally efficient. In contrast, probability entropy performed the worst, highlighting the need for advanced UQ approaches for this task.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086485","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 semiclassical model of the immediate temperature distribution surrounding the track of heavy ions with therapeutic energies.","authors":"Martin Rädler, Niayesh Afshordi, Reza Taleei, Katia Parodi, Ramin Abolfath, Julie Lascaud","doi":"10.1088/1361-6560/add83b","DOIUrl":"10.1088/1361-6560/add83b","url":null,"abstract":"<p><p><i>Objective.</i>Spikes of high temperature and pressure are created in the vicinity of heavy ions, especially at the Bragg peak. The expected subsequent thermoacoustic effects are however not well understood. In particular, the distribution of the densely packed primary interactions has not been considered in molecular dynamics (MDs) simulations or shock wave solutions. In this work, we derive a dedicated model to describe the primary interactions and their radial distribution, applicable to the modeling of acoustic and thermodynamic effects at the nanoscale.<i>Approach.</i>Starting from first principles, we assemble a semiclassical model of the energy loss of the primary heavy ions, consistent with the expected linear energy transfer and parametrized with the distance from the track. Based on the interaction energies, we then disentangle the primary energy depositions, i.e. the primary excitations and binding energies of the secondary electrons. Thereby we obtain the radial distribution of the primary interactions, independent of empirical parameters. Our theoretical description is kept general, however, numerical results are presented for protons stopped in water. Validity and uncertainties of our model are analyzed in detail.<i>Main results.</i>Following from the sought radial energy distribution, we find that the primary interactions are the dominant energy depositions below a radius of 1 nm. This can give rise to thermal spikes as high as 10³ K even for low-<i>Z</i>projectiles, such as protons stopped in water. The presented model is valid down to primary proton energies of approximately 0.5 MeV.<i>Significance.</i>Our results can be used to revise the thermodynamic modeling at the nanoscale and investigate their potential involvement in the intriguing biological response to novel modalities such as FLASH or spatially fractionated radiotherapies. Also, our findings can be integrated into microscale track structure Monte Carlo codes, or<i>ab initio</i>MD simulations, for more accurate modeling in the nanometer domain.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015679","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":"DP-MDM: detail-preserving MR reconstruction via multiple diffusion models.","authors":"Mengxiao Geng, Jiahao Zhu, Ran Hong, Qiqing Liu, Dong Liang, Qiegen Liu","doi":"10.1088/1361-6560/add83a","DOIUrl":"10.1088/1361-6560/add83a","url":null,"abstract":"<p><p><i>Objective.</i>Magnetic resonance imaging (MRI) is critical in medical diagnosis and treatment by capturing detailed features, such as subtle tissue changes, which help clinicians make precise diagnoses. However, the widely used single diffusion model has limitations in accurately capturing more complex details. This study aims to address these limitations by proposing an efficient method to enhance the reconstruction of detailed features in MRI.<i>Approach.</i>We present a detail-preserving reconstruction method that leverages multiple diffusion models (DP-MDM) to extract structural and detailed features in the k-space domain, which complements the image domain. Since high-frequency information in k-space is more systematically distributed around the periphery compared to the irregular distribution of detailed features in the image domain, this systematic distribution allows for more efficient extraction of detailed features. To further reduce redundancy and enhance model performance, we introduce virtual binary masks with adjustable circular center windows that selectively focus on high-frequency regions. These masks align with the frequency distribution of k-space data, enabling the model to focus more efficiently on high-frequency information. The proposed method employs a cascaded architecture, where the first diffusion model recovers low-frequency structural components, with subsequent models enhancing high-frequency details during the iterative reconstruction stage.<i>Main results.</i>Experimental results demonstrate that DP-MDM achieves superior performance across multiple datasets. On the<i>T1-GE brain</i>dataset with 2D random sampling at<i>R</i>= 15, DP-MDM achieved 35.14 dB peak signal-to-noise ratio (PSNR) and 0.8891 structural similarity (SSIM), outperforming other methods. The proposed method also showed robust performance on the<i>Fast-MRI</i>and<i>Cardiac MR</i>datasets, achieving the highest PSNR and SSIM values.<i>Significance.</i>DP-MDM significantly advances MRI reconstruction by balancing structural integrity and detail preservation. It not only enhances diagnostic accuracy through improved image quality but also offers a versatile framework that can potentially be extended to other imaging modalities, thereby broadening its clinical applicability.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143994148","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":"On factors that influence deep learning-based dose prediction of head and neck tumors.","authors":"Ruochen Gao, Prerak Mody, Chinmay Rao, Frank Dankers, Marius Staring","doi":"10.1088/1361-6560/adcfeb","DOIUrl":"10.1088/1361-6560/adcfeb","url":null,"abstract":"<p><p><i>Objective.</i>This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.<i>Approach.</i>We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture, and noise on model performance. Two datasets are used: a public dataset (OpenKBP) and an in-house clinical dataset. Model performance is primarily evaluated using two metrics: dose score and dose-volume histogram (DVH) score.<i>Main results.</i>High-resolution inputs improve prediction accuracy (dose score and DVH score) by 8.6%-13.5% compared to low resolution. Using a combination of CT, planning target volumes, and organs-at-risk as input significantly enhances accuracy, with improvements of 57.4%-86.8% over using CT alone. Integrating mean absolute error (MAE) loss with value-based and criteria-based DVH loss functions further boosts DVH score by 7.2%-7.5% compared to MAE loss alone. In the robustness analysis, most models show minimal degradation under Poisson noise (0-0.3 Gy) but are more susceptible to adversarial noise (0.2-7.8 Gy). Notably, certain models, such as SwinUNETR, demonstrate superior robustness against adversarial perturbations.<i>Significance.</i>These findings highlight the importance of optimizing deep learning models and provide valuable guidance for achieving more accurate and reliable radiotherapy dose prediction.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010503","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":"Whole-body CT-to-PET synthesis using a customized transformer-enhanced GAN.","authors":"Bangyan Xu, Ziwei Nie, Jian He, Aimei Li, Ting Wu","doi":"10.1088/1361-6560/add8dd","DOIUrl":"10.1088/1361-6560/add8dd","url":null,"abstract":"<p><p><i>Background</i>. Positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET-CT) is a multi-modality medical imaging technique widely used for screening and diagnosis of lesions and tumors, in which, CT can provide detailed anatomical structures, while PET can show metabolic activities. Nevertheless, it has disadvantages such as long scanning time, high cost, and relatively high radiation doses.<i>Purpose</i>. We propose a deep learning model for the whole-body CT-to-PET synthesis task, generating high-quality synthetic PET images that are comparable to real ones in both clinical relevance and diagnostic value.<i>Material</i>. We collect 102 pairs of 3D CT and PET scans, which are sliced into 27 240 pairs of 2D CT and PET images (training: 21,855 pairs, validation: 2810 pairs, testing: 2575 pairs).<i>Methods</i>. We propose a transformer-enhanced generative adversarial network (GAN) for whole-body CT-to-PET synthesis task. The CPGAN model uses residual blocks and fully connected transformer residual blocks to capture both local features and global contextual information. A customized loss function incorporating structural consistency is designed to improve the quality of synthesized PET images.<i>Results</i>. Both quantitative and qualitative evaluation results demonstrate effectiveness of the CPGAN model. The mean and standard variance of NRMSE, PSNR and SSIM values on test set are(16.90±12.27)×10-4,28.71±2.67and0.926±0.033, respectively, outperforming other seven state-of-the-art models. Three radiologists independently and blindly evaluated and gave subjective scores to 100 randomly chosen PET images (50 real and 50 synthetic). By Wilcoxon signed rank test, there are no statistical differences between the synthetic PET images and the real ones.<i>Conclusions</i>. Despite the inherent limitations of CT images to directly reflect biological information of metabolic tissues, CPGAN model effectively synthesizes satisfying PET images from CT scans, which has potential in reducing the reliance on actual PET-CT scans.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079585","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":"Recognizing artery segments on carotid ultrasonography using embedding concatenation of deep image and vision-language models.","authors":"Chung-Ming Lo, Sheng-Feng Sung","doi":"10.1088/1361-6560/add8db","DOIUrl":"10.1088/1361-6560/add8db","url":null,"abstract":"<p><p><i>Objective.</i>Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arteries is the preferred first-line examination due to its ease of use, noninvasive, and absence of radiation exposure. This study proposed an automated classification model for the common carotid artery (CCA), carotid bulb, internal carotid artery (ICA), and external carotid artery (ECA) to enhance the quantification of carotid artery examinations.<i>Approach</i>. A total of 2943 B-mode ultrasound images (CCA: 1563; bulb: 611; ICA: 476; ECA: 293) from 288 patients were collected. Three distinct sets of embedding features were extracted from artificial intelligence networks including pre-trained DenseNet201, vision transformer, and echo contrastive language-image pre-training models using deep learning architectures for pattern recognition. These features were then combined in a support vector machine classifier to interpret the anatomical structures in B-mode images.<i>Main results</i>. After ten-fold cross-validation, the model achieved an accuracy of 82.3%, which was significantly better than using individual feature sets, with a<i>p</i>-value of <0.001.<i>Significance.</i>The proposed model could make carotid artery examinations more accurate and consistent with the achieved classification accuracy. The source code is available athttps://github.com/buddykeywordw/Artery-Segments-Recognition.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144079583","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":"Multi-basis-image reconstruction from conventional data acquired in standard CT.","authors":"Buxin Chen, Zheng Zhang, Dan Xia, Emil Y Sidky, Xiaochuan Pan","doi":"10.1088/1361-6560/add789","DOIUrl":"10.1088/1361-6560/add789","url":null,"abstract":"<p><p><i>Objective.</i>We investigate and develop an algorithm to invert the well-established non-linear data model in standard computed tomography (CT) for numerically accurate and stable reconstruction of multi (⩾2)-basis images directly from a set of conventional data collected with a single spectrum in standard CT.<i>Approach.</i>Using the basis-region technique to reduce the number of voxel values, i.e. unknowns, in the basis images to be reconstructed and the volume-conservation constraint to augment conventional data, we formulate the reconstruction problem (i.e. the inverse problem) as a non-convex optimization program and develop the dynamic non-convex primal-dual (dNCPD) algorithm to empirically solve the optimization program for numerically accurate and stable reconstruction of multi-basis images from conventional data.<i>Main results.</i>We conduct studies to verify numerically the reconstruction accuracy of the dNCPD algorithm with simulated conventional data and also studies to evaluate the stability of the dNCPD algorithm with real conventional data that contain noise and other physical factors. The study results reveal that the dNCPD algorithm can numerically accurately and stably yield multi-basis images and virtual monochromatic images from conventional data.<i>Significance.</i>The work can be of theoretic interest and practical implication as it reveals the possibility of yielding multi-basis images from conventional data in standard CT, instead of data collected in dual-energy, multi-spectra, or photon-counting CT.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988626","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":"Patient CT-based simulation study of secondary-electron-bremsstrahlung imaging for range verification in proton therapy: comparison with prompt gamma and PET imaging for simplified proton pencil beam and SOBP irradiation scenarios.","authors":"Takuya Yabe, Munetaka Nitta, Mitsutaka Yamaguchi, Marco Pinto, Naoki Kawachi, Katia Parodi","doi":"10.1088/1361-6560/add4b7","DOIUrl":"10.1088/1361-6560/add4b7","url":null,"abstract":"<p><p><i>Objective.</i>Secondary electron bremsstrahlung (SEB) imaging, along with prompt gamma (PG) and positron emission tomography (PET) imaging, has been proposed as an<i>in vivo</i>range verification tool for proton therapy. This study presents the first simulation based on patient computed tomography (CT) data to investigate the feasibility of SEB imaging for range verification in proton therapy, while comparing the characteristics of SEB imaging with those of PG and PET imaging.<i>Approach.</i>A Monte Carlo simulation was performed using patient CT data for the irradiation of monoenergetic pencil beams and spread-out Bragg peak proton beams. The physical characteristics of SEB imaging were analyzed at three different anatomical sites and compared with those of PG and PET imaging.<i>Main results</i>. In all the treatment cases, SEB imaging exhibited higher production rates than PG and PET imaging, particularly in the regions with high CT values along the beam path. Although the SEB signal was more affected by scattering and absorption than the PET or PG signals, sufficient statistical counts for range verification (∼3 × 10^-3SEBs/proton) could potentially be detected outside the patient geometry. For pencil beam cases, the SEB and PET fall-offs were located 4-5 mm proximal to the dose fall-off, while the PG fall-off was located 0-1 mm distal to it.<i>Significance.</i>Results suggest that SEB imaging has the potential to offer a real-time range verification tool (by comparing measured and expected images), particularly for treating shallow-seated tumors using proton pencil-beam scanning delivery. Thus, this study represents a significant step towards the clinical application of range verification based on SEB imaging and promotes future efforts in this direction.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019928","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}