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

Abstract

<b>Objectives:</b> 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. <b>Methods:</b> 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). <b>Results:</b> 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. <b>Conclusion:</b> 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.