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

• Published in: Acta radiologica (1987) Vol. 64; no. 5; p. 1831
• Main Authors: Kobayashi, Takuma, Nishii, Tatsuya, Umehara, Kensuke, Ota, Junko, Ohta, Yasutoshi, Fukuda, Tetsuya, Ishida, Takayuki
• Format: Journal Article
• Language: English
• Published: England 01.05.2023
• Subjects: Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary Artery Disease - diagnostic imaging; Coronary Stenosis - diagnostic imaging; Deep Learning; Humans; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Retrospective Studies; noise reduction; deep learning; image postprocessing; Coronary computed tomography angiography; convolutional neural network

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. To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reducti...