Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth. (May 2023)

• Record Type: Journal Article
• Authors: Kobayashi, Takuma; Nishii, Tatsuya; Umehara, Kensuke; Ota, Junko; Ohta, Yasutoshi; Fukuda, Tetsuya; Ishida, Takayuki
• Is Part Of: Acta radiologica. Volume 64:Number 5(2023)
• Page Start: 1831
• Page End: 1840
• Publication Date: 2023-05
• Subjects: Coronary computed tomography angiography -- noise reduction -- deep learning -- convolutional neural network -- image postprocessing; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/02841851221141656
• Languages: English
• ISSNs: 0284-1851

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.