Physica Medica-European Journal of Medical Physics最新文献_第3页

Twenty years of medical physics education and training in Ghana: past, current, and future perspectives 加纳二十年的医学物理教育和培训：过去、现在和未来的展望。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2026-01-01 DOI: 10.1016/j.ejmp.2025.105713

Mark Pokoo-Aikins , Edem Kwabla Sosu , Theresa Bebaaku Dery , Shiraz Issahaku , Eric Clement K Adisson , Theophilus Akumea Sackey , Mary Adu-Poku , Samuel Nii Adu Tagoe , Kofi Okyere Akyea-Larbi , Joseph Richmond Fianko , Stephen Inkoom , Francis Hasford

Background

Medical physics education remains challenging in developing countries due to limited expertise and infrastructure. Ghana initiated its first comprehensive postgraduate medical physics program in 2004, representing a pioneering effort in sub-Saharan Africa. This study evaluates the program’s development and impact over two decades.

Methods

A comprehensive review analyzed Ghana’s medical physics education evolution from 2004 to 2024, examining institutional growth, curriculum development, workforce distribution, and international partnerships. Data sources included enrollment statistics, graduate outcomes, professional registries, and collaboration records with organizations including the International Atomic Energy Agency (IAEA).

Results

Ghana established a structured pathway comprising two-year Master’s, four-year doctoral, and 12–24 month clinical internship programs. The program has so far produced 195 graduates (22 % international students), demonstrating regional impact. Currently, 76 qualified medical physicists serve in clinical practice (51 %), academia (45 %), and regulatory roles (4 %). Ghana achieved IAEA Regional Designated Centre status and established partnerships with institutions in Norway, Canada, and the United States. However, workforce distribution remains uneven, with Greater Accra and Ashanti regions containing 80 % of practitioners. Challenges include inadequate clinical training infrastructure and limited research funding.

Conclusions

Ghana’s program demonstrates a successful model for developing medical physics education in resource-limited settings through strategic international partnerships. The evolution from overseas training dependency to regional leadership provides valuable insights for other developing nations. Future sustainability requires curriculum modernization, expanded clinical residencies, enhanced research infrastructure, and continued international collaboration to maintain regional leadership in medical physics education.

背景：由于专业知识和基础设施有限，医学物理教育在发展中国家仍然具有挑战性。加纳于2004年启动了其第一个综合研究生医学物理学项目，这在撒哈拉以南非洲地区是一项开创性的努力。本研究评估了该计划在过去二十年的发展和影响。方法：全面回顾分析了2004年至2024年加纳医学物理教育的演变，考察了机构增长、课程开发、劳动力分布和国际合作伙伴关系。数据来源包括入学统计、毕业生成果、专业注册以及与国际原子能机构（IAEA）等组织的合作记录。结果：加纳建立了一个结构化的途径，包括两年的硕士，四年的博士和12-24个月的临床实习项目。该项目迄今已培养195名毕业生（其中22%为国际学生），显示出地区影响力。目前，76名合格的医学物理学家从事临床实践（51%）、学术界（45%）和监管工作（4%）。加纳取得了原子能机构区域指定中心的地位，并与挪威、加拿大和美国的机构建立了伙伴关系。然而，劳动力分布仍然不均衡，大阿克拉和阿散蒂地区占从业人员的80%。挑战包括临床培训基础设施不足和研究经费有限。结论：加纳的项目展示了通过战略国际伙伴关系在资源有限的环境中发展医学物理教育的成功模式。从海外培训依赖到地区领导的演变为其他发展中国家提供了宝贵的见解。未来的可持续发展需要课程现代化、扩大临床实习、加强研究基础设施和持续的国际合作，以保持医学物理教育的地区领导地位。

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引用次数: 0

Intrafractional rectum anatomy shape prediction based on 3D point cloud representation in online adaptive radiation therapy 基于三维点云表示的在线适应性放疗直肠解剖形状预测。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-24 DOI: 10.1016/j.ejmp.2025.105705

Wenyu Wang , Zihong Zhou , Ran Wei , Ningyu Wang , Xuena Yan , Yuan Xu , Ningning Lu , Jianrong Dai , Kuo Men

Purpose

This study aimed to develop an anatomical structure generative model to predict intrafractional rectal shape in prostate cancer online adaptive radiation therapy (OART).

Methods

A retrospective analysis was conducted on clinical data from 42 prostate cancer patients treated with online adaptive radiotherapy (OART). Rectal shapes were extracted from MRI scans acquired at the pretreatment (Pre-) and position verification (Pv-) stages, and represented as 3D point clouds. Data augmentation was applied to construct the rectal dataset. Then, we developed SA-UNet, among the earliest generative AI–based models for intrafractional anatomical shape prediction, and benchmarked its performance against two conventional deep learning baseline models.

Results

The SA-UNet model demonstrated superior performance in anatomical structure prediction, yielding the lowest average CD (29.06 ± 12.56 mm) and EMD (4.82 ± 1.31 mm) values and the highest average JAC value (0.69 ± 0.07). Compared with Baseline-MLP, SA-UNet achieved significantly greater consistency across treatment fractions, with reduced variability and fewer outliers (p < 0.025, Bonferroni-adjusted). Meanwhile, the SA-UNet significantly outperformed Baseline-PointCNN, which had the highest average CD (42.85 ± 14.18 mm) and EMD (6.07 ± 1.31 mm), and the lowest JAC (0.62 ± 0.07), all significantly inferior to those of SA-UNet (p < 0.01, Bonferroni-adjusted).

Conclusions

The SA-UNet model showed preliminary feasibility for intrafractional rectal shape prediction in OART, offering potential for early alerting and advancing precision radiotherapy.

目的：本研究旨在建立一种预测前列腺癌在线适应性放射治疗（OART）术中直肠形态的解剖结构生成模型。方法：回顾性分析42例前列腺癌在线适应放疗（OART）患者的临床资料。从预处理（Pre-）和位置验证（Pv-）阶段获得的MRI扫描中提取直肠形状，并表示为3D点云。应用数据增强技术构建直肠数据集。然后，我们开发了SA-UNet，这是最早的基于生成人工智能的解剖形状预测模型之一，并将其性能与两种传统的深度学习基线模型进行了比较。结果：SA-UNet模型在解剖结构预测方面表现优异，平均CD值（29.06±12.56 mm）和EMD值（4.82±1.31 mm）最低，平均JAC值（0.69±0.07）最高。与Baseline-MLP相比，SA-UNet在各治疗阶段的一致性显著提高，变异性减少，异常值减少(p)。结论：SA-UNet模型初步显示了OART患者术中直肠形状预测的可行性，为早期预警和推进精确放疗提供了潜力。

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引用次数: 0

Systematic review and meta-analysis of AI-driven MRI motion artifact detection and correction 人工智能驱动的MRI运动伪影检测和校正的系统综述和荟萃分析。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-23 DOI: 10.1016/j.ejmp.2025.105704

Mojtaba Safari , Zach Eidex , Richard L.J. Qiu , Matthew Goette , Tonghe Wang , Xiaofeng Yang

Background:

To systematically review and perform a meta-analysis of artificial intelligence (AI)-driven methods for detecting and correcting magnetic resonance imaging (MRI) motion artifacts, assessing current developments, effectiveness, challenges, and future research directions.

Methods:

A comprehensive systematic review and meta-analysis were conducted, focusing on deep learning (DL) approaches, particularly generative models, for the detection and correction of MRI motion artifacts. Quantitative data were extracted regarding utilized datasets, DL architectures, and performance metrics.

Results:

DL, particularly generative models, shows promise for reducing motion artifacts and improving image quality; however, limited generalizability, reliance on paired training data, and risk of visual distortions remain key challenges that motivate standardized datasets and reporting.

Conclusions:

AI-driven methods, particularly DL generative models, show significant potential for improving MRI image quality by effectively addressing motion artifacts. However, critical challenges must be addressed, including the need for comprehensive public datasets, standardized reporting protocols for artifact levels, and more advanced, adaptable DL techniques to reduce reliance on extensive paired datasets. Addressing these aspects could substantially enhance MRI diagnostic accuracy, reduce healthcare costs, and improve patient care outcomes.

背景：对人工智能（AI）驱动的磁共振成像（MRI）运动伪影检测和校正方法进行系统回顾和荟萃分析，评估当前的发展、有效性、挑战和未来的研究方向。方法：进行了全面的系统回顾和荟萃分析，重点关注深度学习（DL）方法，特别是生成模型，用于MRI运动伪影的检测和校正。提取了关于所利用的数据集、深度学习架构和性能指标的定量数据。结果：深度学习，特别是生成模型，显示出减少运动伪影和提高图像质量的希望；然而，有限的通用性、对成对训练数据的依赖以及视觉失真的风险仍然是激发标准化数据集和报告的关键挑战。结论：人工智能驱动的方法，特别是DL生成模型，通过有效地处理运动伪影，显示出改善MRI图像质量的巨大潜力。然而，关键的挑战必须得到解决，包括需要全面的公共数据集、工件级别的标准化报告协议，以及更先进、适应性更强的深度学习技术，以减少对大量成对数据集的依赖。解决这些问题可以大大提高MRI诊断的准确性，降低医疗成本，并改善患者的护理结果。

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引用次数: 0

X-ray imaging dosimeter performance in standard and non-standard radiography radiation fields in terms of air kerma x射线成像剂量计在标准和非标准放射照相辐射领域的性能。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-23 DOI: 10.1016/j.ejmp.2025.105687

Nikola Kržanović , Paula Toroi , Ivana Komatina , Bartel Jansen , Argiro Boziari , Margarida Caldeira , Maysa Costa de Castro , Alessia Ciccotelli , Ana Fernandes , Andrea Kojić , Kam Lee , Carita Lindholm , Stefan Pojtinger , Erinc Reyhanoglu , Luigi Rinaldi , Amra Šabeta , Elisabeth Salomon , Siarhei Saroka , Claudia Silvestri , Vladimir Sochor , Miloš Živanović

Introduction

X-ray medical imaging developments have introduced needs for updated dosimetry practices.

Methods

Performance of commercially available dosimeters used for air kerma measurements in diagnostic and interventional radiology was examined. Ionization chambers and X-ray multimeters were tested in a wide range of air kerma rates, photon energies (using standard and non-standard radiation qualities), and angles of incidence with different dosimeter orientation and rotation. Stability and repeatability of the measured value, the influence of pulse duration, non-linearity of dosimeter response, energy and angular dependence were studied against the IEC 61674:2024 limits of variation. Energy response was tested using the standard RQR and RQT radiation qualities defined in IEC 61267:2005, as well as non-standard copper-filtered beams with added 0.9 mm Cu filtration.

Results

Most dosimeters complied with the IEC 61674:2024 standard limits of variation, for both standard and non-standard radiation fields. In some cases, observed performance was significantly better than the current limits allowing for the introduction of more stringent values.

Conclusion

Modification of the performance requirements was proposed, considering differences between reference-class and field-class dosimeters, while introducing more stringent requirements for reference-class dosimeters.

简介：x射线医学成像的发展已经引入了更新剂量学实践的需求。方法：对市面上可买到的用于诊断和介入放射学测量空气剂量计的性能进行检验。电离室和x射线万用表在不同的剂量计取向和旋转下，在大范围内测试了空气速率、光子能量（使用标准和非标准辐射质量）和入射角。根据IEC 61674:2024的变化限值，研究了测量值的稳定性和重复性、脉冲持续时间、剂量计响应的非线性、能量和角度依赖性的影响。能量响应测试使用IEC 61267:2005中定义的标准RQR和RQT辐射质量，以及添加0.9 mm Cu过滤的非标准铜过滤光束。结果：大多数剂量仪在标准和非标准辐射场均符合IEC 61674:2024标准变异限。在某些情况下，观察到的性能明显好于允许引入更严格值的当前限制。结论：考虑到参比级剂量仪与现场级剂量仪的差异，提出了对参比级剂量仪性能要求的修改，同时对参比级剂量仪提出了更严格的要求。

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引用次数: 0

Monte Carlo simulation of a cabinet kilovoltage X-ray irradiator 柜式千伏x射线辐照器的蒙特卡罗模拟。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-22 DOI: 10.1016/j.ejmp.2025.105708

E. Theodoridou , R. Dong , C.C. King , G. Poludniowski , P. Häring , E. Hain , C. Litou , E. Voutou , P. Foka , N. Sammut , J. Seco , M.F. Spadea

Introduction:

We present a rigorously defined dosimetric Monte Carlo (MC) model of the MultiRad225 kilovoltage X-ray irradiator, which is validated using experimental techniques. Previous MC studies performed with the MultiRad225 lacked rigorous dosimetric validation.

Methods:

Experimental measurements are conducted with an ionization chamber and radiochromic film with beam energies of 119, 160, and 200 kV. MC simulations are conducted mimicking each experiment. Both beam quality (half-value layer, HVL) and quantity (dose rate) are assessed with these methods.

Results:

MC simulated dose rates and HVL values show close agreement with experimental results across varying source-to-surface distances (SSD) for each beam energy with most dose rate simulations falling within 5% of the experimentally measured values.

Conclusions:

This work provides a validated MC model as a foundation for future studies with the MultiRad225. The MC model utilizes a comprehensive geometry which accurately captures relevant physical phenomena.

我们提出了一个严格定义的剂量蒙特卡罗（MC）模型，用于MultiRad225千电压x射线辐照器，并使用实验技术进行了验证。先前使用MultiRad225进行的MC研究缺乏严格的剂量学验证。方法：利用离子化室和射线致变色膜，分别以119,160和200kv束流进行实验测量。对每个实验进行MC模拟。用这些方法评估光束质量（半值层，HVL）和数量（剂量率）。结果：MC模拟的剂量率和HVL值与每个光束能量在不同源-表面距离（SSD）上的实验结果非常吻合，大多数剂量率模拟落在实验测量值的5%以内。结论：这项工作为未来的MultiRad225研究提供了一个有效的MC模型。MC模型利用了一个全面的几何结构，准确地捕捉了相关的物理现象。

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引用次数: 0

Validation and optimization of dual-energy CT for accurate dose calculation in photon radiotherapy of brain metastases 双能CT在脑转移瘤光子放疗中精确剂量计算的验证与优化。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-22 DOI: 10.1016/j.ejmp.2025.105702

Gary Razinskas, Chiara Jegelka, Stefan Weick, Johannes Kraft, Paul Lutyj, Andrea Wittig-Sauerwein, Anne Richter

Purpose

Dual-energy computed tomography (DECT) offers enhanced tissue contrast and the potential for direct electron density mapping, but its role in accurate dose calculation for radiotherapy remains underexplored. This study evaluates whether DECT-derived reconstructions can reproduce conventional single-energy CT (SECT)-based dose distributions for stereotactic brain radiotherapy while minimizing workflow disruption.

Methods

A Gammex Advanced Electron Density Phantom (Sun Nuclear, Melbourne, FL, USA) was scanned using a twin-spiral DECT protocol on a Somatom go.Open Pro CT scanner (Siemens Healthineers, Forchheim, Germany). Calibration curves were derived for virtual monoenergetic images (VMI) across 40–190 keV and for direct electron density (Rho) reconstructions. An optimal VMI energy (VMI_opt) was identified to match the SECT calibration. Retrospective dose calculations were performed on 24 stereotactic treatment plans for brain metastases, comparing standard SECT, VMI_opt, and Rho datasets. Dose-volume histogram (DVH) metrics and gamma analyses assessed accuracy.

Results

An optimal VMI energy of 72 keV closely replicated the standard SECT calibration. VMI_opt-based dose calculations showed minimal dosimetric deviations, with mean differences of 0.27%, 0.37%, and 0.41% for D_95%, D_98%, and D_99%, respectively. Rho-based reconstructions exhibited slightly larger variations, averaging 0.75%, 0.92%, and 1.02% for the same parameters. Gamma analysis demonstrated pass rates above 99.6% for VMI_opt and above 99.1% for Rho using 1%/1 mm criteria, even in high-dose and steep-gradient regions.

Conclusion

Both 72 keV VMI and Rho-based DECT reconstructions enable accurate, clinically acceptable dose calculations for stereotactic radiotherapy of brain metastases. These findings support DECT’s integration into clinical workflows, enhancing planning precision without increasing operational complexity.

目的：双能计算机断层扫描（DECT）提供增强的组织对比和直接电子密度成像的潜力，但其在放射治疗精确剂量计算中的作用仍未得到充分探讨。本研究评估了ect衍生的重建是否能够再现传统的基于单能量CT （SECT）的立体定向脑放疗剂量分布，同时最大限度地减少工作流程中断。方法：Gammex高级电子密度幻影（Sun Nuclear, Melbourne， FL， USA）在Somatom go上使用双螺旋DECT协议进行扫描。Open Pro CT扫描仪（Siemens Healthineers, Forchheim, Germany）。推导了40- 190kev范围内的虚拟单能图像（VMI）和直接电子密度（Rho）重建的校准曲线。确定了与SECT校准相匹配的最佳VMI能量（VMIopt）。对24种脑转移性立体定向治疗方案进行回顾性剂量计算，比较标准SECT、VMIopt和Rho数据集。剂量-体积直方图（DVH）指标和伽马分析评估了准确性。结果：最佳VMI能量为72 keV，与标准的SECT校准值基本一致。基于vmiopt的剂量计算显示最小的剂量学偏差，D95%、D98%和D99%的平均差异分别为0.27%、0.37%和0.41%。基于rho的重建显示出稍大的变化，对于相同的参数，平均为0.75%，0.92%和1.02%。Gamma分析表明，即使在高剂量和陡峭梯度区域，使用1%/1 mm标准，VMIopt的通过率高于99.6%，Rho的通过率高于99.1%。结论：72kev VMI和基于rho的DECT重建均能准确、临床可接受的脑转移性立体定向放疗剂量计算。这些发现支持DECT整合到临床工作流程中，在不增加操作复杂性的情况下提高计划精度。

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引用次数: 0

Fast personalized CT dose calculations with GPUMCD 快速个性化CT剂量计算与GPUMCD

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-20 DOI: 10.1016/j.ejmp.2025.105693

Ronan O. Lefol , Yannick Lemaréchal , Alexandre Sagona , Jonathan Boivin , Philippe Joubert , Philippe Després

Purpose:

The continuous increase of population dose due to ever-rising Computed Tomography (CT) examinations has called for more personalized dose estimations in medical imaging - a far from trivial task. This study aims to demonstrate a GPU-enabled pipeline combining automatic segmentation with GPU Monte Carlo Dose (GPUMCD) simulations to provide patient-specific dose-to-organ CT dosimetry reports using existing patient CT images.

Methods:

A dynamic representation of the CT imaging process was reproduced within GPUMCD using information in DICOM headers, complemented by in-house exposure measurements, and validated in homogeneous and anthropomorphic phantoms. A dose pipeline was implemented using GPUMCD and a pre-trained open-source nnU-net model (TotalSegmentator). Dose-to-organ dosimetry was obtained for images from a lung cancer screening program and stored in DICOM-compliant Structured Reports.

Results:

GPUMCD calculated dose values were within 5.5% of measurements for all phantoms and investigated conditions. Utilizing one A100-SXM4-40GB GPU, the average pipeline runtime was 6 min and 06 s per CT study. The GPU-driven simulation and segmentation operation took 46% (2 min and 7 s) of the total runtime, and data processing (file reading, conversion, and writing) occupied the remaining 54%.

Conclusion:

This work demonstrates the ability to generate patient-specific three-dimensional dose distributions in CT within a few seconds and the subsequent feasibility of performing fully automated mass personalized dose-to-organ calculations. The pipeline ingests and produces DICOM-compliant data compatible with clinical and research environments, enabling routine imaging dosimetry and large-scale retroactive dosimetry studies.

目的：由于计算机断层扫描（CT）检查的不断增加，人口剂量不断增加，要求在医学成像中进行更个性化的剂量估计-这远非微不足道的任务。本研究旨在展示一个GPU支持的流水线，将自动分割与GPU蒙特卡罗剂量（GPUMCD）模拟相结合，利用现有的患者CT图像提供患者特异性剂量到器官的CT剂量测定报告。方法：在GPUMCD中使用DICOM头中的信息再现CT成像过程的动态表示，辅以内部暴露测量，并在均匀和仿人模型中进行验证。使用GPUMCD和预训练的开源nnU-net模型（TotalSegmentator）实现了剂量管道。从肺癌筛查项目中获得图像的剂量-器官剂量测定，并存储在符合dicom的结构化报告中。结果：GPUMCD计算的剂量值在所有幻像和调查条件下的测量值的5.5%以内。使用一台A100-SXM4-40GB GPU，每次CT研究的平均管道运行时间为6分钟06秒。gpu驱动的仿真和分割操作占用了总运行时间的46%（2分钟7秒），数据处理（文件读取、转换和写入）占用了剩余的54%。结论：这项工作证明了在几秒钟内在CT上生成患者特异性三维剂量分布的能力，以及随后进行全自动质量个性化剂量-器官计算的可行性。该管道获取并生成符合dicom标准的数据，与临床和研究环境兼容，实现常规成像剂量学和大规模追溯剂量学研究。

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引用次数: 0

Statistical uncertainty-aware dual-path dilated convolution fusion framework for Monte Carlo dose denoising: Enhancing accuracy and efficiency in radiotherapy planning 蒙特卡罗剂量去噪的统计不确定性感知双路径扩展卷积融合框架：提高放疗计划的准确性和效率

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-19 DOI: 10.1016/j.ejmp.2025.105700

Xingyu Lu , Yongbao Li , Linghong Zhou , Ting Song

Purpose

Monte Carlo (MC)-based dose calculation provides high accuracy in radiotherapy but is limited by statistical uncertainty (SU), which introduces noise and increases computational demands. This study proposes a Statistical Uncertainty-aware deep learning framework with a Dual-Path Dilated Convolution Fusion architecture to improve the accuracy and efficiency of MC dose denoising.

Methods

We designed a three-channel convolutional neural network that integrates noisy MC dose distributions, corresponding SU maps, and CT images. The dual-path structure combines standard and dilated convolutions to extract both local anatomical features and global contextual information. The model was trained and validated on 69 clinical IMRT plans from three tumor sites (head-and-neck, brain, and lung), each with six levels of simulated noise generated by a GPU-based MC engine. High-particle MC doses served as the ground truth. A total of six sub-models were trained for different noise levels, and performance was evaluated using mean dose error (MDE), gamma passing rate (GPR), and dose-volume histogram (DVH) analysis.

Results

The SU-aware model outperformed its two-channel SU-agnostic counterpart across all tumor sites, with MDEs reduced to (0.84 ± 0.26)% for head-and-neck, (0.85 ± 0.21)% for brain, and (0.36 ± 0.09)% for lung. GPRs exceeded 97 % (3 %/3 mm) with ≥ 1 × 10⁵ histories. Denoising was completed within seconds, enabling real-time application.

Conclusions

By explicitly incorporating SU into the network, the proposed model achieves fast, accurate dose denoising.

目的基于蒙特卡罗（MC）的放射治疗剂量计算具有较高的准确性，但受统计不确定性（SU）的限制，统计不确定性引入了噪声，增加了计算量。为了提高MC剂量去噪的准确性和效率，提出了一种具有双路径扩展卷积融合架构的统计不确定性感知深度学习框架。方法设计了一种三通道卷积神经网络，该网络集成了噪声MC剂量分布、相应SU图和CT图像。双路径结构结合标准卷积和扩张卷积来提取局部解剖特征和全局上下文信息。该模型在来自三个肿瘤部位（头颈部、脑部和肺部）的69个临床IMRT计划上进行了训练和验证，每个计划都有由基于gpu的MC引擎产生的六个级别的模拟噪声。高粒子MC剂量是基本事实。针对不同的噪声水平，共训练了6个子模型，并使用平均剂量误差（MDE）、伽马通过率（GPR）和剂量-体积直方图（DVH）分析对其性能进行了评估。结果su感知模型在所有肿瘤部位均优于双通道su不确定模型，头颈部的MDEs降至（0.84±0.26）%，脑部的MDEs降至（0.85±0.21）%，肺部的MDEs降至（0.36±0.09）%。GPRs≥97% (3% / 3mm)，≥1 × 105次。去噪在几秒钟内完成，实现实时应用。结论通过明确地将SU纳入网络，该模型实现了快速、准确的剂量去噪。

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引用次数: 0

The potential of a coronary artery-specific deep learning CT reconstruction algorithm for improvement in image quality of abdominal CT angiography with special reference to small arterial visibility 冠状动脉特异性深度学习CT重建算法在提高腹部CT血管造影图像质量方面的潜力，特别是在小动脉可见性方面。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-18 DOI: 10.1016/j.ejmp.2025.105706

Masashi Kinjyo , Akihiro Nishie , Taiyo Oshiro , Keisuke Oshiro , Shota Nakano , Nanae Tsuchiya

Background

/Introduction: We sought to clarify the potential of Precise IQ Engine (PIQE) for the visualization of small arteries in abdominal computed tomography angiography (CTA).

Methods

CT images scanned with a 320-multidetector row CT were generated using four reconstruction methods: Adaptive Iterative Dose Reduction 3D (AIDR 3D), Forward projected model-based Iterative Reconstruction SoluTion (FIRST), Advanced intelligent Clear-IQ Engine (AiCE), and PIQE. Phantom study: We evaluated spatial resolution via the modulation transfer function (MTF) and the slit phantom assessment. We also measured the noise power spectrum (NPS) and CT number to evaluate image noise. Clinical study: Abdominal CTA images from five renal donor candidates were used to evaluate the visibility of four small abdominal arteries per person. The differences in visibility scores evaluated by two radiologists were analyzed using Sheffe’s multiple comparison method.

Results

The 10 % values of MTF were 0.54 for AIDR 3D, 0.67 for FIRST, 0.66 for AiCE, and 0.66 for PIQE. In the slit phantom assessment, FIRST, AiCE, and PIQE showed no clear difference in attenuation profiling curves, whereas the maximum spatial resolution for AIDR 3D was the worst. The NPS of FIRST was the highest, whereas that of PIQE was the lowest for the low spatial frequency range. The standard deviation of CT number with PIQE was the lowest, whereas that of FIRST was the highest. The median visibility score of PIQE was significantly higher than those of AIDR 3D, FIRST and AiCE, respectively, for each radiologist (p < 0.05).

Conclusion

PIQE may improve the image quality of abdominal CTA.

背景/简介：我们试图阐明精确IQ引擎（PIQE）在腹部计算机断层血管造影（CTA）中显示小动脉的潜力。方法：采用自适应迭代剂量减少3D （AIDR 3D）、基于正演投影模型的迭代重建方案（FIRST）、高级智能Clear-IQ引擎（AiCE）和PIQE四种重建方法生成320排多探测器CT扫描的CT图像。幻影研究：我们通过调制传递函数（MTF）和狭缝幻影评估来评估空间分辨率。我们还测量了噪声功率谱（NPS）和CT数来评估图像的噪声。临床研究：使用5例候选肾供体的腹部CTA图像来评估每个人的4条腹部小动脉的可见性。采用谢菲多重比较法对两名放射科医师评定的可视性评分差异进行分析。结果：AIDR 3D的MTF 10%值为0.54，FIRST为0.67，AiCE为0.66，PIQE为0.66。在狭缝幻象评估中，FIRST、AiCE和PIQE在衰减剖面曲线上没有明显差异，而AIDR 3D的最大空间分辨率最差。在低空间频率范围内，FIRST的NPS最高，PIQE的NPS最低。PIQE的CT数标准差最小，而FIRST的CT数标准差最大。每位放射科医师PIQE的中位可视性评分分别显著高于AIDR 3D、FIRST和AiCE (p)。结论：PIQE可改善腹部CTA的图像质量。

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引用次数: 0

HetMS-MC: A framework for heterogeneous multiscale Monte Carlo modelling in radiation medicine 辐射医学中异构多尺度蒙特卡罗建模框架。

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics

Pub Date : 2025-12-16 DOI: 10.1016/j.ejmp.2025.105689

Elizabeth M. Fletcher, Rowan M. Thomson

Background:

Radiation medicine involves processes spanning many length scales and there is interest in modelling across scales to advance knowledge, particularly for prospective treatment approaches, e.g., gold nanoparticle-enhanced radiation therapy (GNPT). Previously, implementation of this type of multiscale modelling in radiation medicine has been problem-based, with little standardization and no established framework.

Purpose:

To introduce a framework for heterogeneous multiscale Monte Carlo (HetMS-MC) modelling in radiation medicine.

Methods:

The presented framework includes considerations for the creation and analysis of HetMS-MC models, including model development, simulation setup, uncertainty analysis and quantification. The framework is demonstrated through two examples: (1) tumour model; (2) GNPT scenario. Both are implemented in EGSnrc and consist of a cm-scale tumour containing a region of interest comprised of 650 micron-scale cells in which specific energy is scored.

Results:

The tumour model demonstrates the need for multiscale modelling as the HetMS-MC model captures differences in specific energy distributions from varying input parameters such as cell arrangement and MC random number seed that are not seen in conventional MC simulations. The GNPT model highlights the importance of multi-scale bridging in the development of HetMS-MC models.

Conclusions:

A general HetMS-MC framework is established and used to develop two models relevant to radiation medicine. This framework allows for efficient simulation of radiation physics processes across many length scales for arbitrary treatment modalities.

背景：放射医学涉及跨越许多长度尺度的过程，人们对跨尺度建模以推进知识感兴趣，特别是对于前瞻性治疗方法，例如金纳米粒子增强放射治疗（GNPT）。以前，在放射医学中实施这种类型的多尺度建模是基于问题的，几乎没有标准化，也没有建立框架。目的：介绍辐射医学中异构多尺度蒙特卡罗（HetMS-MC）模型的框架。方法：提出的框架包括对建立和分析HetMS-MC模型的考虑，包括模型开发、仿真设置、不确定性分析和量化。通过两个实例对该框架进行了论证：(1)肿瘤模型；(2) GNPT情景。两者都在EGSnrc中实现，由cm级肿瘤组成，该肿瘤包含由650微米级细胞组成的感兴趣区域，其中特定能量被评分。结果：肿瘤模型证明了多尺度建模的必要性，因为HetMS-MC模型从不同的输入参数（如细胞排列和MC随机数种子）中捕获了特定能量分布的差异，这些差异在传统的MC模拟中是看不到的。GNPT模型强调了多尺度桥接在HetMS-MC模型开发中的重要性。结论：建立了一个通用的HetMS-MC框架，并用于开发两个与放射医学相关的模型。该框架允许在任意治疗方式的许多长度尺度上有效地模拟辐射物理过程。

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引用次数: 0