We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.
- Authors
Kobayashi, Takuma; Nishii, Tatsuya; Umehara, Kensuke; Ota, Junko; Ohta, Yasutoshi; Fukuda, Tetsuya; Ishida, Takayuki
- 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.
- Subjects
FOUR-dimensional imaging; NOISE control; CORONARY angiography; CORONARY artery stenosis; CONVOLUTIONAL neural networks
- Publication
Acta Radiologica, 2023, Vol 64, Issue 5, p1831
- ISSN
0284-1851
- Publication type
Article
- DOI
10.1177/02841851221141656