Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.

Abstract

Objectives: To exploit the capability of super-resolution deep learning reconstruction (SR-DLR) to save radiation exposure from coronary CT angiography (CCTA) and assess its impact on image quality, coronary plaque quantification and characterization, and stenosis severity analysis.

Materials and methods: This prospective study included 50 patients who underwent low-dose (LD) and subsequent ultra-low-dose (ULD) CCTA scans. LD CCTA images were reconstructed with hybrid iterative reconstruction (HIR) and ULD CCTA images were reconstructed with HIR and SR-DLR. The objective parameters and subjective scores were compared. Coronary plaques were classified into three components: necrotic, fibrous or calcified content, with absolute volumes (mm³) recorded, and further characterized by percentage of calcified content. The four main coronary arteries were evaluated for the presence of stenosis. Moreover, 48 coronary segments in 9 patients were evaluated for the presence of significant stenosis, with invasive coronary angiography as a reference.

Results: Effective dose decreased by 60% from LD to ULD CCTA scans (2.01 ± 0.84 mSv vs. 0.80 ± 0.34 mSv, p < 0.001). ULD SR-DLR was non-inferior or even superior to LD HIR in terms of image quality and showed excellent agreements with LD HIR on the plaque volumes, characterization, and stenosis analysis (ICCs > 0.8). Moreover, there was no evidence of a difference in detecting significant coronary stenosis between the LD HIR and ULD SR-DLR (AUC: 0.90 vs. 0.89; p = 1.0).

Conclusions: SR-DLR led to significant radiation dose savings from CCTA while ensuring high image quality and excellent performance in coronary plaque and stenosis analysis.

Key points: Question How can radiation dose for coronary CT angiography be reduced without compromising image quality or affecting clinical decisions? Finding Super-resolution deep learning reconstruction (SR-DLR) algorithm allows for 60% dose reduction while ensuring high image quality and excellent performance in coronary plaque and stenosis analysis. Clinical relevance Dose optimization via SR-DLR has no detrimental effect on image quality, coronary plaque quantification and characterization, and stenosis severity analysis, which paves the way for its implementation in clinical practice.