Deep learning reconstruction algorithm for coronary CT angiography in assessing obstructive coronary artery disease caused by calcified lesions: the clinical application value

Abstract

Objective: To investigate the image quality of coronary CT angiography (CCTA) subjected to deep learning-based reconstruction algorithm (DLR) method and its diagnostic performance for stenosis caused by coronary calcified lesions. Methods: We enrolled 33 consecutive patients with known or suspected coronary artery disease (CAD) who underwent CCTA and subsequently invasive coronary angiography (ICA) within 1 month in the department of radiology, Peking Union Medical College Hospital between February 2020 and February 2021. Among them, there are 26 males and 7 females, age range from 45 to 86 (61.9±9.0) years. The CCTA images were reconstructed with DLR and hybrid iterative reconstruction (HIR). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated on the aorta root, left main artery, proximal left anterior descending, left circumflex, and right coronary artery of the CCTA images and were used to evaluate the objective image quality (IQ). Subjective IQ score was graded using Likert four-point scale (1 for excellent and 4 for poor). The diagnostic performance of obstructive coronary artery disease caused by calcified lesions on CCTA subjected to DLR and HIR methods were evaluated using ICA as the reference standard. Results: A total of 123 lesions in 33 patients were included in the analysis. Image noise of DLR image was significantly lower than that on HIR image(defined as the standard deviation of the attenuation values in the aortic root: 18.12±3.66 vs 24.19±5.71, P<0.001), CNR and SNR of DLR image in the aortic root were higher (CNR:43.83±23.73 vs 26.38±9.69, P<0.001,SNR:26.66±7.83 vs 21.23±8.65, P<0.001). Subjective scores of DLR was better than HIR image (1.12±0.41 vs 1.46±0.60,P<0.001). The sensitivity, specificity and accuracy of DLR and HIR images for diagnosing obstructive coronary artery disease caused by calcified lesions were 100.0%, 77.4%, 78.9% and 100.0%, 63.5%, 65.9%%, respectively. The number of false positive cases on DLR image decreased by 38% compared with HIR. Conclusions: Artificial intelligence based DLR can significantly reduce the image noise and improve the image quality of CCTA. DLR helps to improve the diagnostic performance of CCTA in assessing obstructive coronary artery disease caused by calcified lesions, which may have good clinical application value.