Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth

Abstract

Background To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses. Purpose To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning. Material and Methods \We retrospectively collected 100 retrospective ECG-gated CCTAs. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently. Results DLNR CCTAs showed 64.5% lower image noise ( P < 0.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison ( P > 0.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77–0.99) with original CCTAs. Conclusion Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis.