医学物理
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显示 2025年07月28日， 星期一 新的列表
- [1] arXiv:2507.18677 (交叉列表自 cs.CV) [中文pdf, pdf, html, 其他]
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标题： HeartUnloadNet：从舒张状态预测无负荷心脏几何形状的弱监督循环一致图网络标题： HeartUnloadNet: A Weakly-Supervised Cycle-Consistent Graph Network for Predicting Unloaded Cardiac Geometry from Diastolic States评论： 代码可在 https://github.com/SiyuMU/Loaded2UnNet 获取主题： 计算机视觉与模式识别 (cs.CV) ; 医学物理 (physics.med-ph)
未加载的心脏几何形态（即心脏无腔内压力的状态）作为一个有价值的零应力和零应变参考，对于个性化心脏功能生物力学建模至关重要，有助于理解健康和疾病生理，并预测心脏干预的效果。然而，从临床图像中估计未加载几何形态仍然是一个具有挑战性的任务。传统方法依赖于逆向有限元（FE）求解器，这些求解器需要迭代优化且计算成本高。在本工作中，我们引入了HeartUnloadNet，这是一种深度学习框架，可以直接从舒张末期（ED）网格预测未加载的左心室（LV）形状，并显式地结合生物物理先验知识。该网络接受任意大小的网格以及生理参数，如ED压力、心肌刚度尺度和纤维螺旋方向，并输出相应的未加载网格。它采用图注意力架构，并采用循环一致性策略以实现双向（加载和卸载）预测，允许部分自监督，从而提高准确性并减少对大型训练数据集的需求。在20,700个跨越多种LV几何形态和生理条件的FE仿真上进行训练和测试，HeartUnloadNet实现了亚毫米级精度，平均DSC为0.986，HD为0.083 cm，同时将推理时间缩短至每例仅0.02秒，比传统的逆向FE求解器快超过10^5倍，并且显著更准确。消融研究证实了该架构的有效性。值得注意的是，循环一致性设计使模型即使在仅有200个训练样本的情况下也能保持97%的DSC。因此，这项工作提出了一个可扩展且准确的逆向FE求解器替代方案，为未来的实时临床应用提供了支持。
The unloaded cardiac geometry (i.e., the state of the heart devoid of luminal pressure) serves as a valuable zero-stress and zero-strain reference and is critical for personalized biomechanical modeling of cardiac function, to understand both healthy and diseased physiology and to predict the effects of cardiac interventions. However, estimating the unloaded geometry from clinical images remains a challenging task. Traditional approaches rely on inverse finite element (FE) solvers that require iterative optimization and are computationally expensive. In this work, we introduce HeartUnloadNet, a deep learning framework that predicts the unloaded left ventricular (LV) shape directly from the end diastolic (ED) mesh while explicitly incorporating biophysical priors. The network accepts a mesh of arbitrary size along with physiological parameters such as ED pressure, myocardial stiffness scale, and fiber helix orientation, and outputs the corresponding unloaded mesh. It adopts a graph attention architecture and employs a cycle-consistency strategy to enable bidirectional (loading and unloading) prediction, allowing for partial self-supervision that improves accuracy and reduces the need for large training datasets. Trained and tested on 20,700 FE simulations across diverse LV geometries and physiological conditions, HeartUnloadNet achieves sub-millimeter accuracy, with an average DSC of 0.986 and HD of 0.083 cm, while reducing inference time to just 0.02 seconds per case, over 10^5 times faster and significantly more accurate than traditional inverse FE solvers. Ablation studies confirm the effectiveness of the architecture. Notably, the cycle-consistent design enables the model to maintain a DSC of 97% even with as few as 200 training samples. This work thus presents a scalable and accurate surrogate for inverse FE solvers, supporting real-time clinical applications in the future.
- [2] arXiv:2507.18850 (交叉列表自 eess.IV) [中文pdf, pdf, html, 其他]
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标题： X-核磁共振波谱成像的敏感度图估计标题： Estimating Sensitivity Maps for X-Nuclei Magnetic Resonance Spectroscopic Imaging主题： 图像与视频处理 (eess.IV) ; 医学物理 (physics.med-ph) ; 定量方法 (q-bio.QM)
本研究的目的是在成像X-核时估算敏感度图，这些X-核在整个视野中可能没有显著的存在。我们提出通过求解一个最小二乘问题来估算线圈的敏感度，其中每一行对应于给定体素的敏感度的单独估计。多个估计来自光谱的多个区间（通过光谱学）、动态成像的多个时间点，或利用光谱激发时的多个频率。本文中提出的方法称为L2最优方法，与常用的RefPeak方法进行了比较，该方法使用能量最高的光谱区间来估算敏感度图。当成像数值幻象时，L2最优方法能产生更准确的敏感度图，并且在使用超极化丙酮酸作为对比剂进行超极化MRI成像大脑、胰腺和心脏时，显示出更高的信噪比。L2最优方法能够通过从测量中提取更多信息来更好地估算敏感度。
The purpose of this research is to estimate sensitivity maps when imaging X-nuclei that may not have a significant presence throughout the field of view. We propose to estimate the coil's sensitivities by solving a least-squares problem where each row corresponds to an individual estimate of the sensitivity for a given voxel. Multiple estimates come from the multiple bins of the spectrum with spectroscopy, multiple times with dynamic imaging, or multiple frequencies when utilizing spectral excitation. The method presented in this manuscript, called the L2 optimal method, is compared to the commonly used RefPeak method which uses the spectral bin with the highest energy to estimate the sensitivity maps. The L2 optimal method yields more accurate sensitivity maps when imaging a numerical phantom and is shown to yield a higher signal-to-noise ratio when imaging the brain, pancreas, and heart with hyperpolarized pyruvate as the contrast agent with hyperpolarized MRI. The L2 optimal method is able to better estimate the sensitivity by extracting more information from the measurements.
- [3] arXiv:2507.19282 (交叉列表自 eess.IV) [中文pdf, pdf, 其他]
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标题： SAM2-Aug：基于先验知识的自适应放疗目标体积自动分割增强方法 使用Segment Anything Model 2标题： SAM2-Aug: Prior knowledge-based Augmentation for Target Volume Auto-Segmentation in Adaptive Radiation Therapy Using Segment Anything Model 2评论： 26页，10图主题： 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)
目的：准确的肿瘤分割对于自适应放疗（ART）至关重要，但仍然耗时且依赖用户。分割任意模型2（SAM2）在基于提示的分割中显示出前景，但在肿瘤准确性方面存在困难。我们提出了基于先验知识的增强策略，以提高SAM2在ART中的性能。 方法：引入了两种策略来改进SAM2：（1）使用先验MR图像和标注作为上下文输入，（2）通过随机边界框扩展和掩码腐蚀/膨胀来提高提示的鲁棒性。所得模型SAM2-Aug在One-Seq-Liver数据集（31名肝癌患者的115个MRI）上进行了微调和测试，并在Mix-Seq-Abdomen（88个MRI，28名患者）和Mix-Seq-Brain（86个MRI，37名患者）上进行了评估，无需重新训练。 结果：SAM2-Aug在所有数据集上均优于卷积、基于Transformer和基于提示的模型，在肝脏、腹部和大脑中的Dice分数分别为0.86、0.89和0.90。它在不同类型的肿瘤和成像序列中表现出强大的泛化能力，并在边界敏感指标中表现更好。 结论：结合先验图像和增强提示多样性显著提高了分割精度和泛化能力。SAM2-Aug为ART中的肿瘤分割提供了一个强大且高效的解决方案。代码和模型将在https://github.com/apple1986/SAM2-Aug发布。
Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for prompt-based segmentation but struggles with tumor accuracy. We propose prior knowledge-based augmentation strategies to enhance SAM2 for ART. Methods: Two strategies were introduced to improve SAM2: (1) using prior MR images and annotations as contextual inputs, and (2) improving prompt robustness via random bounding box expansion and mask erosion/dilation. The resulting model, SAM2-Aug, was fine-tuned and tested on the One-Seq-Liver dataset (115 MRIs from 31 liver cancer patients), and evaluated without retraining on Mix-Seq-Abdomen (88 MRIs, 28 patients) and Mix-Seq-Brain (86 MRIs, 37 patients). Results: SAM2-Aug outperformed convolutional, transformer-based, and prompt-driven models across all datasets, achieving Dice scores of 0.86(liver), 0.89(abdomen), and 0.90(brain). It demonstrated strong generalization across tumor types and imaging sequences, with improved performance in boundary-sensitive metrics. Conclusions: Incorporating prior images and enhancing prompt diversity significantly boosts segmentation accuracy and generalizability. SAM2-Aug offers a robust, efficient solution for tumor segmentation in ART. Code and models will be released at https://github.com/apple1986/SAM2-Aug.
交叉提交 (展示 3 之 3 条目 )
- [4] arXiv:2503.22139 (替换) [中文pdf, pdf, 其他]
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标题： 基于先验模型无关时空高斯表示的时间分辨动态CBCT重建（PMF-STGR）标题： Time-resolved dynamic CBCT reconstruction using prior-model-free spatiotemporal Gaussian representation (PMF-STGR)评论： 25页，5图主题： 医学物理 (physics.med-ph) ; 机器学习 (cs.LG) ; 图像与视频处理 (eess.IV)
基于3D高斯表示的动态CBCT重建框架（PMF-STGR）可快速准确地重建动态CBCT序列。PMF-STGR包含三个主要组件：一组密集的3D高斯用于重建动态序列的参考帧CBCT；另一组3D高斯用于捕捉三级、从粗到细的运动基组件（MBCs）以建模扫描内运动；以及基于CNN的运动编码器，用于求解MBCs的投影特定时间系数。通过时间系数缩放，学习到的MBCs将组合成变形矢量场，将参考CBCT变形为投影特定的时间分辨CBCT，以捕捉动态运动。由于3D高斯的强大表示能力，PMF-STGR可以从标准的3D CBCT扫描中以“一次训练”的方式重建动态CBCT，而无需使用任何先验解剖或运动模型。我们使用XCAT体模仿真和真实患者扫描评估了PMF-STGR。使用的指标包括图像相对误差、结构相似性指数度量、肿瘤质心误差和特征点定位误差，以评估求解的动态CBCT和运动的准确性。PMF-STGR在与最先进的基于INR的方法PMF-STINR的比较中显示出明显的优势。与PMF-STINR相比，PMF-STGR将重建时间减少了50%，同时重建出更少模糊且运动精度更高的图像。凭借改进的效率和准确性，PMF-STGR增强了动态CBCT成像在潜在临床转化中的适用性。
Time-resolved CBCT imaging, which reconstructs a dynamic sequence of CBCTs reflecting intra-scan motion (one CBCT per x-ray projection without phase sorting or binning), is highly desired for regular and irregular motion characterization, patient setup, and motion-adapted radiotherapy. Representing patient anatomy and associated motion fields as 3D Gaussians, we developed a Gaussian representation-based framework (PMF-STGR) for fast and accurate dynamic CBCT reconstruction. PMF-STGR comprises three major components: a dense set of 3D Gaussians to reconstruct a reference-frame CBCT for the dynamic sequence; another 3D Gaussian set to capture three-level, coarse-to-fine motion-basis-components (MBCs) to model the intra-scan motion; and a CNN-based motion encoder to solve projection-specific temporal coefficients for the MBCs. Scaled by the temporal coefficients, the learned MBCs will combine into deformation vector fields to deform the reference CBCT into projection-specific, time-resolved CBCTs to capture the dynamic motion. Due to the strong representation power of 3D Gaussians, PMF-STGR can reconstruct dynamic CBCTs in a 'one-shot' training fashion from a standard 3D CBCT scan, without using any prior anatomical or motion model. We evaluated PMF-STGR using XCAT phantom simulations and real patient scans. Metrics including the image relative error, structural-similarity-index-measure, tumor center-of-mass-error, and landmark localization error were used to evaluate the accuracy of solved dynamic CBCTs and motion. PMF-STGR shows clear advantages over a state-of-the-art, INR-based approach, PMF-STINR. Compared with PMF-STINR, PMF-STGR reduces reconstruction time by 50% while reconstructing less blurred images with better motion accuracy. With improved efficiency and accuracy, PMF-STGR enhances the applicability of dynamic CBCT imaging for potential clinical translation.
- [5] arXiv:2505.24793 (替换) [中文pdf, pdf, html, 其他]
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标题： AFIRE：几何不一致多光谱CT的准确快速图像重建算法标题： AFIRE: Accurate and Fast Image Reconstruction Algorithm for Geometric-inconsistent Multispectral CT评论： 39页，16图，1表主题： 数值分析 (math.NA) ; 医学物理 (physics.med-ph)
对于非线性多光谱计算机断层扫描（CT），当不同X射线能量谱下的扫描几何结构不一致或不匹配时，准确且快速的图像重建具有挑战性。受此启发，我们提出了一种准确且快速的图像REconstruction（AFIRE）算法，以解决轻微全扫描情况下的此类问题。从连续（分别离散）设置中，我们发现所涉及的非线性映射在某些特殊点（例如零点）处的导数算子（梯度）可以表示为一个对角算子（矩阵）与非常小规模矩阵的组合（块乘法）。该对角算子由X射线变换（投影矩阵）组成。基于这些见解，通过利用简化的牛顿方法提出了AFIRE算法。在适当条件下，我们建立了所提出算法的收敛理论。此外，还进行了数值实验，以验证所提出的算法能够在完全几何不一致的双能CT中准确有效地重建基图像，无论是无噪声还是有噪声的投影数据。特别地，所提出的算法在准确性和效率方面显著优于一些最先进的方法。最后，还展示了所提出算法的灵活性和可扩展性。
For nonlinear multispectral computed tomography (CT), accurate and fast image reconstruction is challenging when the scanning geometries under different X-ray energy spectra are inconsistent or mismatched. Motivated by this, we propose an Accurate and Fast Image REconstruction (AFIRE) algorithm to address such problems in the case of mildly full scan. From the continuous (resp. discrete) setting, we discover that the derivative operator (gradient) of the involved nonlinear mapping at some special points, for example, at zero, can be represented as a composition (block multiplication) of a diagonal operator (matrix) composed of X-ray transforms (projection matrices) and a very small-scale matrix. Based on these insights, the AFIRE algorithm is proposed by leveraging the simplified Newton method. Under proper conditions, we establish the convergence theory of the proposed algorithm. Furthermore, numerical experiments are also carried out to verify that the proposed algorithm can accurately and effectively reconstruct the basis images in completely geometric-inconsistent dual-energy CT with noiseless and noisy projection data. Particularly, the proposed algorithm significantly outperforms some state-of-the-art methods in terms of accuracy and efficiency. Finally, the flexibility and extensibility of the proposed algorithm are also demonstrated.