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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07476 |
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| _version_ | 1866911100747907072 |
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| author | Brunet, Joseph Chestnutt, Lisa Chourrout, Matthieu Dejea, Hector Sabarigirivasan, Vaishnavi Lee, Peter D. Cook, Andrew C. |
| author_facet | Brunet, Joseph Chestnutt, Lisa Chourrout, Matthieu Dejea, Hector Sabarigirivasan, Vaishnavi Lee, Peter D. Cook, Andrew C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cardiotensor: A Python Library for Orientation Analysis and Tractography in 3D Cardiac Imaging Brunet, Joseph Chestnutt, Lisa Chourrout, Matthieu Dejea, Hector Sabarigirivasan, Vaishnavi Lee, Peter D. Cook, Andrew C. Computational Engineering, Finance, and Science 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. |
| title | Cardiotensor: A Python Library for Orientation Analysis and Tractography in 3D Cardiac Imaging |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2508.07476 |