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Pericoronary adipose tissue feature analysis in CT calcium score images with comparison to coronary CTA (2401.15554v1)

Published 28 Jan 2024 in cs.CV

Abstract: We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary CTA (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images. To identify coronaries in CTCS images that have subtle visual evidence of vessels, we registered CTCS with paired CCTA images having coronary labels. We developed a novel axial-disk method giving regions for analyzing PCAT features in three main coronary arteries. We analyzed novel hand-crafted and radiomic features using univariate and multivariate logistic regression prediction of MACE and compared results against those from CCTA. Registration accuracy was sufficient to enable the identification of PCAT regions in CTCS images. Motion or beam hardening artifacts were often present in high-contrast CCTA but not CTCS. Mean HU and volume were increased in both CTCS and CCTA for MACE group. There were significant positive correlations between some CTCS and CCTA features, suggesting that similar characteristics were obtained. Using hand-crafted/radiomics from CTCS and CCTA, AUCs were 0.82/0.79 and 0.83/0.77 respectively, while Agatston gave AUC=0.73. Preliminarily, PCAT features can be assessed from three main coronary arteries in non-contrast CTCS images with performance characteristics that are at the very least comparable to CCTA.

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Authors (13)
  1. Yingnan Song (6 papers)
  2. Hao Wu (623 papers)
  3. Juhwan Lee (15 papers)
  4. Justin Kim (7 papers)
  5. Ammar Hoori (11 papers)
  6. Tao Hu (146 papers)
  7. Vladislav Zimin (1 paper)
  8. Mohamed Makhlouf (1 paper)
  9. Sadeer Al-Kindi (9 papers)
  10. Sanjay Rajagopalan (5 papers)
  11. Chun-Ho Yun (3 papers)
  12. Chung-Lieh Hung (3 papers)
  13. David L. Wilson (13 papers)

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