AI-based, automated chamber volumetry from gated, non-contrast CT

Abstract

Background: Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT) scans can be useful for potential screening of heart failure.

Objectives: To validate a new, fully automated, AI-based method for cardiac volume and myocardial mass quantification from NCCT scans compared to contrasted CT Angiography (CCTA).

Methods: Of a retrospectively collected cohort of 1051 consecutive patients, 420 patients had both NCCT and CCTA scans at mid-diastolic phase, excluding patients with cardiac devices. Ground truth values were obtained from the CCTA scans.

Results: The NCCT volume computation shows good agreement with ground truth values. Volume differences [95% CI ] and correlation coefficients were: -9.6 [-45; 26] mL, r ​= ​0.98 for LV Total, -5.4 [-24; 13] mL, r ​= ​0.95 for LA, -8.7 [-45; 28] mL, r ​= ​0.94 for RV, -5.2 [-27; 17] mL, r ​= ​0.92 for RA, -3.2 [-42; 36] mL, r ​= ​0.91 for LV blood pool, and -6.7 [-39; 26] g, r ​= ​0.94 for LV wall mass, respectively. Mean relative volume errors of less than 7% were obtained for all chambers.

Conclusions: Fully automated assessment of chamber volumes from NCCT scans is feasible and correlates well with volumes obtained from contrast study.

Keywords: Artificial intelligence; Cardiac volumetry; Non-contrast CT; Screening.