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

2025-08-10

AI Summary

This study addresses the challenge of quantitatively characterizing myocardial microstructure from massive high-resolution 3D cardiac imaging volumes. We propose an open-source Python framework that— for the first time—deeply integrates structure tensor analysis with tractography, augmented by parallel computing, tiled pipeline processing, and high-performance computing architectures to enable whole-heart, teravoxel-scale data analysis. The method efficiently reconstructs multiscale myocardial fiber trajectories and accurately extracts key quantitative metrics—including helix angle, imbrication angle, and fractional anisotropy. Our contributions are threefold: (1) enabling myocardial orientation modeling and visualization across organ-to-cellular scales; (2) advancing whole-organ, reproducible, and standardized quantitative analysis of cardiac microstructure; and (3) providing a scalable computational foundation for computational cardiology and patient-specific cardiac modeling.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Computer Vision: Medical and Biological Imaging
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Analyzing 3D cardiomyocyte orientation in heart imaging datasets
• Processing large volumetric datasets for cardiac organization metrics
• Reconstructing continuous cardiomyocyte trajectories via tractography

Innovation

• Python library for 3D cardiac orientation analysis
• Scalable structure tensor analysis implementation
• Tractography for cardiomyocyte trajectory reconstruction