Evaluation of Coronary Artery Image Quality with Knowledge-based Iterative Model Reconstruction

Rationale and Objectives To evaluate knowledge-based iterative model reconstruction (IMR) to improve image quality and reduce radiation dose in coronary computed tomography angiography (cCTA). Materials and Methods We evaluated 45 consecutive cCTA studies, including 25 studies performed with an 80% systolic dose reduction using tube current modulation (TCM). Each study was reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (iDose 4 ), and IMR in a diastolic phase. Additional systolic phase reconstructions were obtained for TCM studies. Mean pixel attenuation value and standard deviation (SD) were measured in the left ventricle and left main coronary artery. Subjective scores were obtained by two independent reviewers on a 5-point scale for definitions of contours of small coronary arteries ( Results There was no significant difference in pixel intensity among FBP, iDose 4 , and IMR ( P  > .8). For diastolic phase images, noise amplitude in the left main coronary artery was reduced by a factor of 1.3 from FBP to iDose 4 (SD = 99 vs. 74; P  = .005) and by a factor of 2.6 from iDose 4 to IMR (SD = 74 vs. 28; P 4 (SD = 322 vs. 142; P 4 to IMR (SD = 142 vs. 48; P 4 and FBP ( P Conclusions IMR reduces intravascular noise on cCTA by 86%–88% compared to FBP, and improves image quality at radiation exposure levels 80% below our standard technique.