Diagnostic Improvements of Deep Learning-Based Image Reconstruction for Assessing Calcification-Related Obstructive Coronary Artery Disease

Objectives: The objective of this study was to explore the diagnostic value of deep learning-based image reconstruction (DLR) and hybrid iterative reconstruction (HIR) for calcification-related obstructive coronary artery disease (CAD) evaluation by using coronary CT angiography (CCTA) images and subtraction CCTA images.Methods: Forty-two consecutive patients with known or suspected coronary artery disease who underwent coronary CTA on a 320-row CT scanner and subsequent invasive coronary angiography (ICA), which was used as the reference standard, were enrolled. The DLR and HIR images were reconstructed as CTA(DLR) and CTA(HIR), and, based on which, the corresponding subtraction CCTA images were established as CTA(sDLR) and CTA(sHIR), respectively. Qualitative images quality comparison was performed by using a Likert 4 stage score, and quantitative images quality parameters, including image noise, signal-to-noise ratio, and contrast-to-noise ratio were calculated. Diagnostic performance on the lesion level was assessed and compared among the four CCTA approaches (CTA(DLR), CTA(HIR), CTA(sDLR), and CTA(sHIR)).Results: There were 166 lesions of 86 vessels in 42 patients (32 men and 10 women; 62.9 +/- 9.3 years) finally enrolled for analysis. The qualitative and quantitative image qualities of CTA(sDLR) and CTA(DLR) were superior to those of CTA(sHIR) and CTA(HIR), respectively. The diagnostic accuracies of CTA(sDLR), CTA(DLR), CTA(sHIR), and CTA(HIR) to identify calcification-related obstructive diameter stenosis were 83.73%, 69.28%, 75.30%, and 65.66%, respectively. The false-positive rates of CTA(sDLR), CTA(DLR), CTA(sHIR), and CTA(HIR) for luminal diameter stenosis >= 50% were 15%, 31%, 24%, and 34%, respectively. The sensitivity and the specificity to identify >= 50% luminal diameter stenosis was 90.91% and 83.23% for CTA(sDLR).Conclusion: Our study showed that deep learning-based image reconstruction could improve the image quality of CCTA images and diagnostic performance for calcification-related obstructive CAD, especially when combined with subtraction technique.

Automated summary

This study aimed to investigate the diagnostic value of deep learning-based image reconstruction and hybrid iterative reconstruction in evaluating calcification-related obstructive coronary artery disease. The results showed that DLR images had superior image quality and diagnostic accuracy compared to HIR images.