Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

Yao, Xiaoling; Zhong, Sihua; Xu, Maolan; Zhang, Guozhi; Yuan, Yuan; Shuai, Tao; Li, Zhenlin

Yao, Xiaoling; Zhong, Sihua; Xu, Maolan; Zhang, Guozhi; Yuan, Yuan; Shuai, Tao; Li, Zhenlin.

Affiliation

Yao X; Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Zhong S; United Imaging Healthcare, Shanghai, China.
Xu M; Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Zhang G; United Imaging Healthcare, Shanghai, China.
Yuan Y; Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Shuai T; Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Li Z; Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.

J Appl Clin Med Phys ; 24(9): e14104, 2023 Sep.

Article in En | MEDLINE | ID: mdl-37485892

Subject(s)

Coronary Artery Disease; Deep Learning; Fractional Flow Reserve, Myocardial; Humans; Computed Tomography Angiography; Constriction, Pathologic; Retrospective Studies; Coronary Angiography/methods; Tomography, X-Ray Computed; Algorithms

Key words

coronary artery disease; coronary computed tomography angiography; deep learning; motion correction

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Coronary Artery Disease / Fractional Flow Reserve, Myocardial / Deep Learning Type of study: Observational_studies / Prognostic_studies Limits: Humans Language: En Journal: J Appl Clin Med Phys Journal subject: BIOFISICA Year: 2023 Document type: Article Affiliation country: China

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