标题： Cardiotensor：用于三维心脏成像方向分析和纤维追踪的Python库 显示英文标题

标题： Cardiotensor: A Python Library for Orientation Analysis and Tractography in 3D Cardiac Imaging

摘要： 理解人体心脏的结构需要对其微观组织在不同尺度上的组织进行分析。 随着基于同步辐射的高分辨率成像技术的出现，现在已经可以以微米级分辨率可视化整个心脏。 然而，将这些大型、复杂的体积数据集转化为可解释的、定量的心脏组织描述符仍然是一个主要挑战。 在此，我们介绍了cardiotensor，一个开源的Python包，旨在量化整个或部分心脏成像数据集中的三维心肌细胞取向。 它提供了结构张量分析的高效、可扩展实现，能够提取方向性度量，如螺旋角（HA）、侵入角（IA）和分数各向异性（FA）。 该包支持达到太字节级的数据集，并针对高性能计算环境进行了优化，包括并行和分块处理流程。 此外，cardiotensor还包括追踪功能，用于重建连续的心肌细胞轨迹。 这使得可以根据分辨率，实现从肌原纤维层面的多尺度肌聚集体可视化。 这些功能能够实现心脏组织的详细结构映射，支持对解剖连续性和区域组织的评估。 显示英文摘要

摘要： Understanding the architecture of the human heart requires analysis of its microstructural organization across scales. With the advent of high-resolution imaging techniques such as synchrotron-based tomography, it has become possible to visualize entire hearts at micron-scale resolution. However, translating these large, complex volumetric datasets into interpretable, quantitative descriptors of cardiac organization remains a major challenge. Here we present cardiotensor, an open-source Python package designed to quantify 3D cardiomyocyte orientation in whole- or partial-heart imaging datasets. It provides efficient, scalable implementations of structure tensor analysis, enabling extraction of directional metrics such as helical angle (HA), intrusion angle (IA), and fractional anisotropy (FA). The package supports datasets reaching teravoxel-scale and is optimized for high-performance computing environments, including parallel and chunk-based processing pipelines. In addition, cardiotensor includes tractography functionality to reconstruct continuous cardiomyocyte trajectories. This enables multi-scale myoaggregate visualization down to the myocyte level, depending on resolution. These capabilities enable detailed structural mapping of cardiac tissue, supporting the assessment of anatomical continuity and regional organization.