医学物理

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新提交 (展示 3 之 3 条目 )

[1] arXiv:2509.03751 [中文pdf, pdf, 其他]

标题： 推进正电子发射断层扫描图像量化：人工智能驱动的方法、临床挑战以及长轴视野正电子发射断层扫描/计算机断层扫描成像中的新兴机遇 显示英文标题

标题： Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging

Fereshteh Yousefirizi, Movindu Dassanayake, Alejandro Lopez, Andrew Reader, Gary J.R. Cook, Clemens Mingels, Arman Rahmim, Robert Seifert, Ian Alberts

期刊参考： PET诊所。2025年8月29日

主题： 医学物理 (physics.med-ph)

MTV越来越被认为是疾病负担的准确估计，具有预后价值，但其实施因需要手动分割图像而耗时，受到阻碍。 使用人工智能驱动方法的自动定量是有前景的。 人工智能驱动的自动量化显著减少了劳动密集型的手动分割，提高了一致性、可重复性和常规临床实践的可行性。 增强的人工智能影像组学提供了肿瘤生物学的全面表征，超越了传统体积指标单独所能提供的内容，捕捉到肿瘤内和肿瘤间的异质性，支持改进的患者分层和治疗计划。 正常器官的人工智能驱动分割通过实现准确的剂量预测和全面的基于器官的影像组学分析，改善了放射配体治疗计划，进一步优化了个性化患者管理。 显示英文摘要

MTV is increasingly recognized as an accurate estimate of disease burden, which has prognostic value, but its implementation has been hindered by the time-consuming need for manual segmentation of images. Automated quantitation using AI-driven approaches is promising. AI-driven automated quantification significantly reduces labor-intensive manual segmentation, improving consistency, reproducibility, and feasibility for routine clinical practice. AI-enhanced radiomics provides comprehensive characterization of tumor biology, capturing intratumoral and intertumoral heterogeneity beyond what conventional volumetric metrics alone offer, supporting improved patient stratification and therapy planning. AI-driven segmentation of normal organs improves radioligand therapy planning by enabling accurate dose predictions and comprehensive organ-based radiomics analysis, further refining personalized patient management.

[2] arXiv:2509.03958 [中文pdf, pdf, 其他]

标题： 通过临床兆伏级放疗束高通量纠缠光子生成用于量子成像和诊疗学 显示英文标题

标题： High-Flux Entangled Photon Generation via Clinical Megavoltage Radiotherapy Beams for Quantum Imaging and Theranostics

Gustavo Olivera, Bashkim Ziberi, Stephen Avery, Heth Devin Skinner, Erno Sajo, Hugo Ribeiro, M. Saiful Huq

评论： 19页，4图，3表，43参考文献

主题： 医学物理 (physics.med-ph)

传统量子纠缠光子的来源，如自发参量下转换，存在通量低的问题，限制了临床应用。 我们研究了临床兆伏特（MV）放疗束是否可以作为双用途光源，在输送治疗剂量的同时，同时生成高通量的纠缠光子对，用于量子幽灵成像、量子诊疗（QTX）及相关应用。 使用GEANT4蒙特卡洛模拟，我们建立了含有2厘米球形肿瘤并加载金纳米颗粒（AuNPs，10 mg/mL）的水等效幻影。 模拟了6、10和15 MV的临床能谱。 关键指标包括511 keV光子对产率、正电子素寿命（时间分辨）、多普勒和能量展宽（能量分辨）、信噪比（SNR），以及从5到15 cm深度的纠缠保持情况。 光子对产率与束流能量和AuNP浓度成比例，在AuNP加载的肿瘤中达到约每戈瑞每立方厘米10^8对。 正电子素寿命偏移（约100 ps）反映了肿瘤氧合和活性氧，提供了潜在的功能生物标志物。 多普勒（1-3 keV）和AuNP引起的线展宽（0.5-1 keV）产生了电子密度、组织成分和纳米颗粒摄取的独特光谱特征。 时间分辨的QTX揭示了氧合和ROS水平，能量分辨的QTX探测了组织密度和原子组成，组合模式实现了全面的诊疗成像。 深度依赖的模拟实现了体素信噪比超过100。 这些发现表明，临床MV束可以作为实用的高通量纠缠光子源。 集成的时间和能量分辨QTX支持功能和光谱肿瘤成像，实现纳米颗粒优化，并将量子成像平台从光学（eV）扩展到治疗（数百keV）范围。 显示英文摘要

Traditional sources of quantum-entangled photons, such as spontaneous parametric down-conversion, suffer from low flux, limiting clinical applications. We investigated whether clinical megavoltage (MV) radiotherapy beams can serve as dual-purpose sources, delivering therapeutic dose while simultaneously generating high-flux entangled photon pairs for quantum ghost imaging, quantum theranostics (QTX), and related applications. Using GEANT4 Monte Carlo simulations, we modeled water-equivalent phantoms containing 2 cm spherical tumors loaded with gold nanoparticles (AuNPs, 10 mg/mL). Clinical spectra at 6, 10, and 15 MV were simulated. Key metrics included 511 keV photon-pair yield, positronium lifetimes (time-resolved), Doppler and energy broadening (energy-resolved), signal-to-noise ratios (SNR), and entanglement retention at depths from 5 to 15 cm. Pair yields scaled with beam energy and AuNP concentration, reaching about 10^8 pairs per Gy per cm^3 in AuNP-loaded tumors. Positronium lifetime shifts (about 100 ps) reflected tumor oxygenation and reactive oxygen species, offering potential functional biomarkers. Doppler (1-3 keV) and AuNP-induced line broadening (0.5-1 keV) produced distinct spectroscopic signatures of electron density, tissue composition, and nanoparticle uptake. Time-resolved QTX revealed oxygenation and ROS levels, energy-resolved QTX probed tissue density and atomic composition, and combined modes enabled comprehensive theranostic imaging. Depth-dependent simulations achieved voxel SNRs exceeding 100. These findings demonstrate that clinical MV beams can serve as practical, high-flux entangled photon sources. Integrated time- and energy-resolved QTX supports functional and spectroscopic tumor imaging, enables nanoparticle optimization, and expands quantum imaging platforms from optical (eV) to therapeutic (hundreds of keV) regimes.

[3] arXiv:2509.04024 [中文pdf, pdf, 其他]

标题： 机电人体心脏模型用于预测心内膜心脏运动 显示英文标题

标题： Electromechanical human heart modeling for predicting endocardial heart motion

Milad Hasani, Alireza Rezania, Sam Riahi

主题： 医学物理 (physics.med-ph) ; 数值分析 (math.NA)

这项工作提出了一种全面且具有临床相关性的双心室电机械人体心脏模型，该模型整合了真实的3D心脏几何结构以及体循环和肺循环的血流动力学。 该模型采用双向流-结构相互作用（FSI）公式，并使用实际的3D血液网格，以准确研究血流对心肌的影响。 它结合了反应-扩散框架和电压依赖性主动应力项，以复制电兴奋与机械收缩之间的联系。 此外，该模型还引入了创新的心外膜边界条件，以模拟相邻组织的刚度和粘性。 该模型复制生理心脏运动的能力已通过Cine磁共振成像（MRI）数据进行了验证，结果显示区域位移模式具有高度一致性。 对右心室的分析表明，基底和中部游离壁经历最大的运动，因此这些区域是植入运动驱动能量收集装置的理想位置。 该经过验证的模型是一个强大的工具，可用于在临床实施前增强我们对心脏生理学的理解并优化治疗干预。 显示英文摘要

This work presents a biventricular electromechanical human heart model that is comprehensive and clinically relevant, integrating a realistic 3D heart geometry with both systemic and pulmonary hemodynamics. The model uses a two-way fluid-structure-interaction (FSI) formulation with actual 3D blood meshes to accurately investigate the effect of blood flow on the myocardium. It couples a reaction-diffusion framework and a voltage-dependent active stress term to replicate the link between electrical excitation and mechanical contraction. Additionally, the model incorporates innovative epicardial boundary conditions to mimic the stiffness and viscosity of neighboring tissues. The model's ability to replicate physiological heart motion was validated against Cine magnetic resonance imaging (MRI) data, which demonstrated a high degree of consistency in regional displacement patterns. The analysis of the right ventricle showed that the basal and mid free walls experience the largest motion, making these regions ideal for implanting motion-driven energy harvesting devices. This validated model is a robust tool for enhancing our understanding of cardiac physiology and optimizing therapeutic interventions before clinical implementation.

交叉提交 (展示 2 之 2 条目 )

[4] arXiv:2509.03866 (交叉列表自 physics.optics) [中文pdf, pdf, html, 其他]

标题： 在一组共同的样品上展示X射线暗场重建方法的一个系列 显示英文标题

标题： Demonstrating a family of X-ray dark-field retrieval approaches on a common set of samples

Samantha J. Alloo, Ying Ying How, Jannis N. Ahlers, David M. Paganin, Michelle K. Croughan, Kaye S. Morgan

主题： 光学 (physics.optics) ; 医学物理 (physics.med-ph)

对未解析样品结构的散射敏感，全场X射线成像中的暗场通道提供了与传统吸收和相位对比方法不同的补充信息。 一系列实验暗场技术和提取算法最近被开发出来，通过高分辨率相机直接解析与暗场相关的局部图像模糊来提取该信号。 尽管生成暗场对比的潜在物理机制在各种方法中通常被定义为类似，但尚未使用相同样品对这些暗场技术进行过比较。 在本文中，使用同步辐射上的三种新兴暗场设置——基于传播、单网格和基于斑点的X射线成像，获取了两个样品的暗场成像数据。 随后，使用多种提取算法重建了暗场图像——一些只需要一次样品曝光，另一些则需要多次；一些执行局部、像素级分析，另一些则在整个图像上全局操作。 我们发现，暗场对比的主要贡献——来自未解析微结构的漫散射或通过较大可能解析结构的多次折射——在所有方法中都被一致恢复，证明了相互一致性。 然而，对于空间变化的结构，一些差异显现出来。 我们将这些差异归因于每个技术具有不同的内部标尺，即暗场提取的敏感度尺度，这受到技术的实验设计和算法假设的影响。 本研究旨在指导暗场成像用户根据其成像目标选择最合适的技術，并激发未来对不同方法中暗场灵敏度和暗场对比来源的研究。 显示英文摘要

Sensitive to scattering from unresolved sample structures, the dark-field channel in full-field X-ray imaging provides complementary information to that offered by conventional attenuation and phase-contrast methods. A range of experimental dark-field techniques and retrieval algorithms have been recently developed to extract this signal by directly resolving dark-field-associated local image blurring with a high-resolution camera. While the underlying physical mechanism that generates dark-field contrast is generally defined similarly across the methods, no comparison of these dark-field techniques using identical samples has been conducted. In this paper, dark-field imaging data from two samples were acquired using three emerging dark-field setups at a synchrotron: propagation-based, single-grid, and speckle-based X-ray imaging. Dark-field images were then reconstructed using a variety of retrieval algorithms--some requiring only a single sample exposure, others multiple; some performing local, pixel-wise analysis, and others operating globally on entire images. We find that the dominant contribution to dark-field contrast--arising from diffuse scattering from unresolved microstructures or multiple refractions through larger, potentially resolved structures--is consistently recovered across all approaches, demonstrating mutual agreement. However, some differences emerge for structures that are spatially varying. We attribute these differences to the idea that each technique has a different internal ruler, a sensitivity scale for dark-field retrieval influenced by both the experimental design and algorithmic assumptions of the technique. This study is intended to guide dark-field imaging users in selecting the most appropriate technique for their imaging goals and to motivate future research into dark-field sensitivity and sources of dark-field contrast across different methods.

[5] arXiv:2509.04437 (交叉列表自 cs.CV) [中文pdf, pdf, html, 其他]

标题： 从线到形状：通过霍夫变换的X射线准直器几何约束分割 显示英文标题

标题： From Lines to Shapes: Geometric-Constrained Segmentation of X-Ray Collimators via Hough Transform

Benjamin El-Zein, Dominik Eckert, Andreas Fieselmann, Christopher Syben, Ludwig Ritschl, Steffen Kappler, Sebastian Stober

主题： 计算机视觉与模式识别 (cs.CV) ; 医学物理 (physics.med-ph)

X射线成像中的准直限制了感兴趣区域（ROI）的曝光，并最小化了施加于患者的辐射剂量。准直阴影的检测是数字放射摄影中重要的基于图像的预处理步骤，当边缘被散射X射线辐射遮挡时，这会带来挑战。无论如何，先验知识表明准直形成多边形形状的阴影是显而易见的。因此，我们引入了一种基于深度学习的分割方法，该方法在几何上固有约束。我们通过结合基于可微分霍夫变换的网络来检测准直边界并增强其提取ROI中心信息的能力。在推理过程中，我们将两个任务的信息结合起来，以生成精确的、受线条约束的分割掩码。我们在多样化的实际X射线图像测试集上展示了准直区域的鲁棒重建，中位数豪斯多夫距离为4.3-5.0毫米。虽然此应用最多涉及四个阴影边界，但我们的方法在本质上不受特定边缘数量的限制。 显示英文摘要

Collimation in X-ray imaging restricts exposure to the region-of-interest (ROI) and minimizes the radiation dose applied to the patient. The detection of collimator shadows is an essential image-based preprocessing step in digital radiography posing a challenge when edges get obscured by scattered X-ray radiation. Regardless, the prior knowledge that collimation forms polygonal-shaped shadows is evident. For this reason, we introduce a deep learning-based segmentation that is inherently constrained to its geometry. We achieve this by incorporating a differentiable Hough transform-based network to detect the collimation borders and enhance its capability to extract the information about the ROI center. During inference, we combine the information of both tasks to enable the generation of refined, line-constrained segmentation masks. We demonstrate robust reconstruction of collimated regions achieving median Hausdorff distances of 4.3-5.0mm on diverse test sets of real Xray images. While this application involves at most four shadow borders, our method is not fundamentally limited by a specific number of edges.

替换提交 (展示 3 之 3 条目 )

[6] arXiv:2504.19149 (替换) [中文pdf, pdf, 其他]

标题： 下肢关节在健康年轻成年人跨越障碍物时的运动学和动力学数据集 显示英文标题

标题： A Kinematic and Kinetic Dataset of Lower Limb Joints During Obstacle Crossing in Healthy Young Adults

Jingwen Huang, Shucong Yin, Zhaokai Chen, Hanyang Xu, Chenglong Fu

主题： 医学物理 (physics.med-ph) ; 系统与控制 (eess.SY)

障碍跨越是人类运动的重要组成部分，特别是对于下肢截肢者，他们面临更高的失衡和跌倒风险。 尽管之前的研究已经探讨了这一任务，但它们通常缺乏对不同障碍高度下整个步态周期中运动学和动力学变化的全面检查。 本研究创建了一个新的数据集，从十名健康成年人在四个不同高度（7.5厘米、15厘米、22.5厘米和30厘米）进行障碍跨越时收集的数据。 记录并分析了运动学和动力学数据（髋关节、膝关节和踝关节的角度和扭矩）。 结果表明，障碍物高度的增加会导致摆动相变长，并且髋关节和膝关节角度（分别增加1.5*和1.0*）以及扭矩显著增加。 相反，踝关节角度和力矩在不同障碍高度下变化很小，表明踝关节的运动策略相对一致。 此外，观察到主导脚和非主导脚之间存在显著的不对称性：主导脚表现出更大的髋关节和膝关节角度以及更一致的踝关节行为，反映了更高的协调性。 这些发现为改善防跌倒策略提供了有价值的生物力学见解，并有助于指导假肢和外骨骼等辅助设备的设计。 显示英文摘要

Obstacle crossing is an essential component of human locomotion, particularly for individuals with lower limb amputations who face elevated risks of imbalance and falls. While prior studies have explored this task, they often lack a comprehensive examination of kinematic and kinetic changes throughout the entire gait cycle across varying obstacle heights. This study creates a novel dataset collected from ten healthy adults performing obstacle crossing at four different heights (7.5 cm, 15 cm, 22.5 cm, and 30 cm). Kinematic and kinetic data (angles and torques of hip, knee, and ankle) were recorded and analyzed. Results indicate that increased obstacle height leads to a longer swing phase and significant increases in both hip and knee joint angles (1.5* and 1.0*, respectively) and torques. In contrast, ankle joint angles and moments exhibited minimal variation across obstacle heights, indicating a relatively consistent movement strategy at the ankle. Furthermore, significant asymmetries were observed between the dominant and non-dominant foot: the dominant foot demonstrated larger hip and knee joint angles and more consistent ankle behavior, reflecting greater coordination. These findings offer valuable biomechanical insights for improving fall prevention strategies and informing the design of assistive devices such as prostheses and exoskeletons.

[7] arXiv:2509.00304 (替换) [中文pdf, pdf, 其他]

标题： 人工智能引导的PET图像重建与多示踪剂成像：新方法、挑战与机遇 显示英文标题

标题： Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities

Movindu Dassanayake, Alejandro Lopez, Andrew Reader, Gary J.R. Cook, Clemens Mingels, Arman Rahmim, Robert Seifert, Ian Alberts, Fereshteh Yousefirizi

期刊参考： PET诊所。2025年8月25日

主题： 医学物理 (physics.med-ph)

LAFOV PET/CT有潜力开启新的应用，例如超低剂量PET/CT成像、多重成像，用于生物标志物开发以及更快的AI驱动重建，但在这些技术能够应用于临床常规之前还需要进一步的工作。 LAFOV PET/CT具有无可匹敌的灵敏度，但其空间分辨率相当于一个轴向视野较短的扫描仪。 AI方法正被越来越多地探索作为提高图像分辨率的潜在途径。 显示英文摘要

LAFOV PET/CT has the potential to unlock new applications such as ultra-low dose PET/CT imaging, multiplexed imaging, for biomarker development and for faster AI-driven reconstruction, but further work is required before these can be deployed in clinical routine. LAFOV PET/CT has unrivalled sensitivity but has a spatial resolution of an equivalent scanner with a shorter axial field of view. AI approaches are increasingly explored as potential avenues to enhance image resolution.

[8] arXiv:2505.03498 (替换) [中文pdf, pdf, html, 其他]

标题： Res-MoCoDiff：用于脑部MRI运动伪影校正的残差引导扩散模型 显示英文标题

标题： Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI

Mojtaba Safari, Shansong Wang, Qiang Li, Zach Eidex, Richard L.J. Qiu, Chih-Wei Chang, Hui Mao, Xiaofeng Yang

主题： 计算机视觉与模式识别 (cs.CV) ; 医学物理 (physics.med-ph)

目标。 脑部MRI中的运动伪影，主要是由刚性头部运动引起的，会降低图像质量并阻碍后续应用。 传统的方法来减轻这些伪影，包括重复采集或运动跟踪，会增加工作流程的负担。 本研究引入了Res-MoCoDiff，这是一种高效的去噪扩散概率模型，专门设计用于MRI运动伪影校正。 方法。 Res-MoCoDiff在前向扩散过程中利用了一种新颖的残差误差转移机制，以从运动失真的图像中纳入信息。 这种机制使模型能够模拟噪声的演变，其概率分布与失真数据的概率分布非常接近，从而实现仅需四步的逆向扩散过程。 该模型采用U-net作为主干网络，将注意力层替换为Swin Transformer块，以增强跨分辨率的鲁棒性。 此外，训练过程集成了一个组合的l1+l2损失函数，这有助于提高图像清晰度并减少像素级误差。 Res-MoCoDiff在使用真实运动仿真框架生成的体外数据集和体内MR-ART数据集上进行了评估。 与已建立的方法进行了比较分析，包括CycleGAN、Pix2pix和具有视觉Transformer主干的扩散模型，使用PSNR、SSIM和NMSE等定量指标。 主要结果。 所提出的方法在去除轻微、中度和重度失真水平的运动伪影方面表现出优越的性能。 Res-MoCoDiff始终取得最高的SSIM值和最低的NMSE值，对于轻微失真，PSNR高达41.91+-2.94 dB。 值得注意的是，平均采样时间减少到每批两个图像切片0.37秒，而传统方法为101.74秒。 显示英文摘要

Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact correction.Approach.Res-MoCoDiff exploits a novel residual error shifting mechanism during the forward diffusion process to incorporate information from motion-corrupted images. This mechanism allows the model to simulate the evolution of noise with a probability distribution closely matching that of the corrupted data, enabling a reverse diffusion process that requires only four steps. The model employs a U-net backbone, with attention layers replaced by Swin Transformer blocks, to enhance robustness across resolutions. Furthermore, the training process integrates a combined l1+l2 loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both an in-silico dataset generated using a realistic motion simulation framework and an in-vivo MR-ART dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and a diffusion model with a vision transformer backbone, using quantitative metrics such as PSNR, SSIM, and NMSE.Main results. The proposed method demonstrated superior performance in removing motion artifacts across minor, moderate, and heavy distortion levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41.91+-2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 seconds per batch of two image slices, compared with 101.74 seconds for conventional approaches.