Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease.

Journal: Journal of magnetic resonance imaging : JMRI
PMID: 36847756

Abstract

BACKGROUND: The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown.

Authors

  • Xi Wu
  • Liping Deng
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
  • Wanjiang Li
    Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
  • Pengfei Peng
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
  • Xun Yue
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
  • Lu Tang
    Department of Communication and Journalism, Texas A&M University.
  • Qian Pu
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
  • Yue Ming
    Beijing Key Laboratory of Work Safety and Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
  • Xiaoyong Zhang
    Clinical Science, Philips Healthcare, Chengdu, China.
  • Xiaohua Huang
  • Yucheng Chen
    Cardiology Division, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Juan Huang
    State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Nankai University, Tianjin 300071, PR China.
  • Jiayu Sun
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.

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