Extended-volume image-derived models of coronary microcirculation

Area of Science:

• Cardiovascular Research
• Bioimaging
• Computational Biology

Background:

• Tissue clearing and high-throughput imaging enable detailed microvasculature analysis.
• Processing terabyte-scale datasets presents significant computational challenges.

Purpose of the Study:

• To develop and integrate 3D image processing steps for analyzing terabyte-scale microvasculature images.
• To extract quantitative morphological data from coronary microvasculature in rat hearts.

Main Methods:

• Acquired high-resolution, extended-volume images of rat coronary microvasculature (700 GB dataset).
• Applied chunk-based image segmentation and graph generation for microvasculature quantification.
• Focused on vessels with diameters up to 15 μm.

Main Results:

• Successfully extracted morphological data for the entire coronary microvasculature ring within 16 hours.
• Identified microvessel lengths ranging from 6 to 300 μm, with a mode skewed towards shorter lengths (16.5 μm).
• Determined vessel diameters ranged from 3 to 15 μm, with a normal distribution centered around 6.5 ± 2 μm.

Conclusions:

• The developed tools and techniques are valuable for microcirculation research.
• The generated dataset will facilitate biophysical mechanism analysis using computational models.