  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.
