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Pericoronary adipose tissue (PCAT) is the deposition of fat in the vicinityof the coronary arteries. It is an indicator of coronary inflammation andassociated with coronary artery disease. Non-invasive coronary CT angiography(CCTA) is presently used to obtain measures of the thickness, volume, andattenuation of fat deposition. However, prior works solely focus on measuringPCAT using semi-automated approaches at the right coronary artery (RCA) overthe left coronary artery (LCA). In this pilot work, we developed a fullyautomated approach for the measurement of PCAT mean attenuation and volume inthe region around both coronary arteries. First, we used a large subset ofpatients from the public ImageCAS dataset (n = 735) to train a 3D fullresolution nnUNet to segment LCA and RCA. Then, we automatically measured PCATin the surrounding arterial regions. We evaluated our method on a held-out testset of patients (n = 183) from the same dataset. A mean Dice score of 83% andPCAT attenuation of -73.81 $\pm$ 12.69 HU was calculated for the RCA, while amean Dice score of 81% and PCAT attenuation of -77.51 $\pm$ 7.94 HU wascomputed for the LCA. To the best of our knowledge, we are the first to developa fully automated method to measure PCAT attenuation and volume at both the RCAand LCA. Our work underscores how automated PCAT measurement holds promise as abiomarker for identification of inflammation and cardiac disease.

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