Comparison of different acceleration factors of artificial intelligence-compressed sensing for brachial plexus MRI imaging: scanning time and image quality.

Journal: BMC medical imaging
PMID: 39543482

Abstract

BACKGROUND: 3D brachial plexus MRI scanning is prone to examination failure due to the lengthy scan times, which can lead to patient discomfort and motion artifacts. Our purpose is to investigate the efficacy of artificial intelligence-assisted compressed sensing (ACS) in improving the acceleration efficiency and maintaining or enhancing the image quality of brachial plexus MR imaging.

Authors

  • Tianxin Cheng
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Beijing, 100050, China.
  • Feifei Li
    School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 102488, China.
  • Xuetao Jiang
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Beijing, 100050, China.
  • Dan Yu
    Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, People's Republic of China.
  • Jie Wei
    Department of Computer Science, City College of New York, New York, USA.
  • Ying Yuan
    Department of Radiology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. Electronic address: yuany83@163.com.
  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.

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