Denoising using deep-learning-based reconstruction for whole-heart coronary MRA with sub-millimeter isotropic resolution at 3 T: a volunteer study

Abstract

PURPOSEThe aim of this study was to assess the usefulness of denoising deep-learning-based reconstruction (dDLR) to improve image quality and vessel delineation in noncontrast 3-T wholeheart coronary magnetic resonance angiography (WHCMRA) with sub-millimeter isotropic resolution (Sub-mm) compared with a standard resolution without dDLR (Standard).METHODSFor 10 healthy volunteers, we acquired the WHCMRA with Sub-mm with and without dDLR and Standard to quantify signal- (SNR) and contrast-to-noise ratio (CNR) and vessel edge signal response (VESR) in all the 3 image types. Two independent readers subjectively graded vessel sharpness and signal homogeneity of 8 coronary segments in each patient. We used Kruskal– Wallis test with Bonferroni correction to compare SNR, CNR, VESR, and the subjective evaluation scores among the 3 image types and weighted kappa test to evaluate inter-reader agreement on the scores.RESULTSSNR was significantly higher with Sub-mm with dDLR (P .05); the subjective signal homogeneity was significantly improved from Sub-mm without dDLR to Standard to Sub-mm with dDLR (P < .001). The inter-reader agreement was excellent (kappa=0.84).CONCLUSIONApplication of dDLR is useful for improving image quality and vessel delineation in the WHCMRA with Sub-mm compared with Standard.