医学物理

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新提交 (展示 2 之 2 条目 )

[1] arXiv:2507.22966 [中文pdf, pdf, 其他]

标题： 纳米材料在医学应用中的生物相容性 显示英文标题

标题： Biocompatibility of Nanomaterials in Medical Applications

Marvellous Eyube, Courage Enuesueke, Marvellous Alimikhena

主题： 医学物理 (physics.med-ph)

生物相容性是纳米材料在医学领域应用中的关键因素，因为这些材料必须与生物系统安全有效地相互作用，才能适用于治疗和诊断用途。 本文研究了纳米材料的生物相容性，重点探讨了它们与生物细胞、组织和免疫系统的相互作用。 关键特性如表面化学性质、尺寸、形状和材料组成被进行了分析，因为它们显著影响生物反应。 文章探讨了纳米材料在医学应用中的作用，包括药物递送、诊断成像和组织工程，同时讨论了提高其生物相容性的挑战。 一个关于CaO-CaP二元系统的案例研究被提出，展示了氧化钙（CaO）和磷酸钙（CaP）纳米颗粒在骨组织工程中的应用。 该系统因其能够模拟骨的矿物成分并促进成骨作用而被广泛研究，突显了其治疗潜力以及在临床环境中确保安全生物相容性的挑战。 文章最后回顾了优化纳米材料生物相容性的策略，并讨论了推动其在医疗治疗中应用的研究未来方向。 显示英文摘要

Biocompatibility is a critical factor in the application of nanomaterials in medical fields, as these materials must interact safely and effectively with biological systems to be viable for therapeutic and diagnostic use. This article investigates the biocompatibility of nanomaterials, focusing on their interactions with biological cells, tissues, and the immune system. Key properties such as surface chemistry, size, shape, and material composition are examined, as they significantly influence the biological response. The article explores the role of nanomaterials in medical applications, including drug delivery, diagnostic imaging, and tissue engineering, while discussing the challenges involved in enhancing their biocompatibility. A case study on the CaO-CaP binary system is presented, showcasing the use of calcium oxide (CaO) and calcium phosphate (CaP) nanoparticles in bone tissue engineering. This system is widely investigated for its ability to mimic the mineral content of bone and promote osteogenesis, highlighting both its therapeutic potential and challenges in ensuring safe biocompatibility in clinical settings. The article concludes by reviewing strategies to optimize the biocompatibility of nanomaterials and discussing future directions for research in advancing their applications in medical treatments.

[2] arXiv:2507.23579 [中文pdf, pdf, html, 其他]

标题： 下肢外骨骼支架点对能量消耗和生物力学的影响 显示英文标题

标题： Impact of a Lower Limb Exosuit Anchor Points on Energetics and Biomechanics

Chiara Lambranzi, Giulia Oberti, Christian Di Natali, Darwin G. Caldwell, Manuela Galli, Elena De Momi, Jesùs Ortiz

评论： 12页，10图

期刊参考： IEEE生物医学工程汇刊（2025）

主题： 医学物理 (physics.med-ph) ; 机器人技术 (cs.RO) ; 信号处理 (eess.SP)

锚点位置是外骨骼设计中一个关键但常被忽视的方面，因为它决定了力量如何与人体相互作用。 这项工作分析了不同锚点位置对步态运动学、肌肉激活和能量消耗的影响。 总共进行了六项实验，11名受试者佩戴XoSoft外骨骼，该外骨骼以五种配置辅助髋关节屈曲。 受试者配备了基于IMU的运动追踪系统、EMG传感器和面罩以测量代谢消耗。 结果表明，将膝关节锚点放置在后侧而将髋关节锚点保持在前部，可以将髋关节屈肌的肌肉激活减少高达10.21%，代谢消耗减少高达18.45%。 即使只有髋关节受到辅助，所有配置也对膝关节和踝关节的运动学产生了影响。 总体而言，没有一种配置在所有受试者中都是最优的，这表明为了最佳传递辅助力，需要采用个性化的方法。 这些发现强调了锚点位置确实对外骨骼的有效性和效率有显著影响。 然而，这些最佳位置是根据个体的外骨骼设计而变化的，未来的研究需要针对个体特征定制骨肌模型，并在临床人群中验证这些结果。 显示英文摘要

Anchor point placement is a crucial yet often overlooked aspect of exosuit design since it determines how forces interact with the human body. This work analyzes the impact of different anchor point positions on gait kinematics, muscular activation and energetic consumption. A total of six experiments were conducted with 11 subjects wearing the XoSoft exosuit, which assists hip flexion in five configurations. Subjects were instrumented with an IMU-based motion tracking system, EMG sensors, and a mask to measure metabolic consumption. The results show that positioning the knee anchor point on the posterior side while keeping the hip anchor on the anterior part can reduce muscle activation in the hip flexors by up to 10.21\% and metabolic expenditure by up to 18.45\%. Even if the only assisted joint was the hip, all the configurations introduced changes also in the knee and ankle kinematics. Overall, no single configuration was optimal across all subjects, suggesting that a personalized approach is necessary to transmit the assistance forces optimally. These findings emphasize that anchor point position does indeed have a significant impact on exoskeleton effectiveness and efficiency. However, these optimal positions are subject-specific to the exosuit design, and there is a strong need for future work to tailor musculoskeletal models to individual characteristics and validate these results in clinical populations.

交叉提交 (展示 1 之 1 条目 )

[3] arXiv:2507.23129 (交叉列表自 eess.IV) [中文pdf, pdf, html, 其他]

标题： MRpro - 基于 PyTorch 的 MR 重建和处理开源包 显示英文标题

标题： MRpro - open PyTorch-based MR reconstruction and processing package

Felix Frederik Zimmermann, Patrick Schuenke, Christoph S. Aigner, Bill A. Bernhardt, Mara Guastini, Johannes Hammacher, Noah Jaitner, Andreas Kofler, Leonid Lunin, Stefan Martin, Catarina Redshaw Kranich, Jakob Schattenfroh, David Schote, Yanglei Wu, Christoph Kolbitsch

评论： 提交至《磁共振成像》

主题： 图像与视频处理 (eess.IV) ; 计算机视觉与模式识别 (cs.CV) ; 医学物理 (physics.med-ph)

我们引入了MRpro，一个基于PyTorch和开放数据格式的开源图像重建包。 该框架包括三个主要领域。 首先，它提供了统一的数据结构，用于一致地操作MRI数据集及其相关元数据（例如，k空间轨迹）。 其次，它提供了一个可组合算子、可逼近泛函和优化算法的库，包括适用于所有常见轨迹的统一傅里叶算子以及用于定量MRI的扩展相位图仿真。 这些组件用于创建可以直接使用的关键重建算法实现。 第三，对于深度学习，MRpro包括数据一致性层、可微分优化层以及最先进的主干网络，并整合公共数据集以促进可重复性。 MRpro作为一项由自动化质量控制支持的协作项目开发。 我们在多个应用中展示了MRpro的通用性，包括笛卡尔、径向和螺旋采集；运动校正重建；心脏MRI指纹识别；学习的空间自适应正则化权重；基于模型的学习图像重建和定量参数估计。 MRpro为MRI图像重建提供了一个可扩展的框架。 以可重复性和可维护性为核心，它促进了协作开发，并为未来的MRI成像研究提供了基础。 显示英文摘要

We introduce MRpro, an open-source image reconstruction package built upon PyTorch and open data formats. The framework comprises three main areas. First, it provides unified data structures for the consistent manipulation of MR datasets and their associated metadata (e.g., k-space trajectories). Second, it offers a library of composable operators, proximable functionals, and optimization algorithms, including a unified Fourier operator for all common trajectories and an extended phase graph simulation for quantitative MR. These components are used to create ready-to-use implementations of key reconstruction algorithms. Third, for deep learning, MRpro includes essential building blocks such as data consistency layers, differentiable optimization layers, and state-of-the-art backbone networks and integrates public datasets to facilitate reproducibility. MRpro is developed as a collaborative project supported by automated quality control. We demonstrate the versatility of MRpro across multiple applications, including Cartesian, radial, and spiral acquisitions; motion-corrected reconstruction; cardiac MR fingerprinting; learned spatially adaptive regularization weights; model-based learned image reconstruction and quantitative parameter estimation. MRpro offers an extensible framework for MR image reconstruction. With reproducibility and maintainability at its core, it facilitates collaborative development and provides a foundation for future MR imaging research.

替换提交 (展示 1 之 1 条目 )

[4] arXiv:2412.13811 (替换) [中文pdf, pdf, html, 其他]

标题： 一种用于估计3D脑肿瘤浸润的轻量级优化框架 显示英文标题

标题： A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration

Jonas Weidner, Michal Balcerak, Ivan Ezhov, André Datchev, Laurin Lux, Lucas Zimmer, Daniel Rueckert, Björn Menze, Benedikt Wiestler

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

胶质母细胞瘤是最具侵袭性的原发性脑肿瘤，由于其扩散的显微浸润，在标准MRI上大多无法检测到，因此给临床带来严重挑战。因此，目前的放疗计划在切除腔周围采用均匀的15毫米边缘，未能捕捉到患者特定的肿瘤扩散。肿瘤生长建模提供了一种有前景的方法来揭示这种隐藏的浸润。然而，基于偏微分方程或物理信息神经网络的方法往往计算量大或过于受限，限制了它们在个体患者中的临床适应性。在本工作中，我们提出了一种轻量级、快速且稳健的优化框架，通过将3D肿瘤浓度拟合到MRI肿瘤分割图同时强制平滑的浓度分布来估计肿瘤浓度。该方法在两个公共数据集上的192名脑肿瘤患者中实现了更优的肿瘤复发预测，优于最先进的基线方法，同时将运行时间从30分钟减少到不到一分钟。此外，我们通过展示该框架能够无缝整合其他成像模态或物理约束，证明了其多功能性和适应性。 显示英文摘要

Glioblastoma, the most aggressive primary brain tumor, poses a severe clinical challenge due to its diffuse microscopic infiltration, which remains largely undetected on standard MRI. As a result, current radiotherapy planning employs a uniform 15 mm margin around the resection cavity, failing to capture patient-specific tumor spread. Tumor growth modeling offers a promising approach to reveal this hidden infiltration. However, methods based on partial differential equations or physics-informed neural networks tend to be computationally intensive or overly constrained, limiting their clinical adaptability to individual patients. In this work, we propose a lightweight, rapid, and robust optimization framework that estimates the 3D tumor concentration by fitting it to MRI tumor segmentations while enforcing a smooth concentration landscape. This approach achieves superior tumor recurrence prediction on 192 brain tumor patients across two public datasets, outperforming state-of-the-art baselines while reducing runtime from 30 minutes to less than one minute. Furthermore, we demonstrate the framework's versatility and adaptability by showing its ability to seamlessly integrate additional imaging modalities or physical constraints.