Effect of New Generation Snapshot Freeze Combined With Deep Learning Image Reconstruction on Image Quality of Coronary Artery Calcifications and Their Quantification.

Abstract

Objective: To evaluate the effectiveness of the new-generation snapshot freeze (SSF2) algorithm combined with Deep Learning Image Reconstruction (DLIR) in improving the image quality of coronary artery calcifications (CAC) and their quantification.

Methods: Coronary artery calcification score (CACS) scans were performed on 69 patients using ECG-triggered noncontrast CT. Four groups of images were reconstructed with SSF2 or without (STD), combined with ASIR-V (Adaptive Statistical Iterative Reconstruction-V) and DLIR: STDASIR-V, STDDLIR, SSF2ASIR-V, and SSF2DLIR. CAC image quality was compared, and inter-observer consistency was evaluated among reconstruction groups. CACS, including the Agatston score (AS), volume score (VS), mass score (MS), and the risk stratification based on AS among groups, were compared.

Results: The consistencies of the inter-observer image quality scores were excellent or good (kappa=0.705 to 0.837). SSF2ASIR-V and SSF2DLIR had significantly higher scores than STDASIR-V and STDDLIR in reducing motion artifacts of calcified plaques (P<0.05), while no significant differences between SSF2ASIR-V and SSF2DLIR, or between STDASIR-V and STDDLIR (P>0.05). There was no significant difference in CT values of vessels, subcutaneous fat, and muscle in CAC images, but the noises of SSF2ASIR-V and STDASIR-V images were significantly higher than those of SSF2DLIR and STDDLIR images (P>0.05). STDASIR-V had the highest CACS values, while SSF2DLIR had the lowest. Using AS in STDASIR-V as the reference, 9 patients (13.04%) in SSF2DLIR and 7 patients (10.14%) in SSF2ASIR-V had a risk stratification reduced, while no change in STDDLIR.

Conclusions: SSF2 and DLIR significantly reduce motion artifacts and image noise in non-contrast CACS CT, respectively. SSF2 reduces CACS values and risk stratification.