Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in CT Angiography

• URL: http://arxiv.org/abs/2311.13100v1
• Date: Wed, 22 Nov 2023 01:59:19 GMT
• Title: Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in CT Angiography
• Authors: Andrew M. Nguyen, Tejas Sudharshan Mathai, Liangchen Liu, Jianfei Liu, Ronald M. Summers
• Abstract summary: Pericoronary adipose tissue (PCAT) is the deposition of fat in the vicinity of the coronary arteries. We developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
• Score: 7.426629740318084
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pericoronary adipose tissue (PCAT) is the deposition of fat in the vicinity of the coronary arteries. It is an indicator of coronary inflammation and associated with coronary artery disease. Non-invasive coronary CT angiography (CCTA) is presently used to obtain measures of the thickness, volume, and attenuation of fat deposition. However, prior works solely focus on measuring PCAT using semi-automated approaches at the right coronary artery (RCA) over the left coronary artery (LCA). In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries. First, we used a large subset of patients from the public ImageCAS dataset (n = 735) to train a 3D full resolution nnUNet to segment LCA and RCA. Then, we automatically measured PCAT in the surrounding arterial regions. We evaluated our method on a held-out test set of patients (n = 183) from the same dataset. A mean Dice score of 83% and PCAT attenuation of -73.81 $\pm$ 12.69 HU was calculated for the RCA, while a mean Dice score of 81% and PCAT attenuation of -77.51 $\pm$ 7.94 HU was computed for the LCA. To the best of our knowledge, we are the first to develop a fully automated method to measure PCAT attenuation and volume at both the RCA and LCA. Our work underscores how automated PCAT measurement holds promise as a biomarker for identification of inflammation and cardiac disease.

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