Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: T2-weighted imaging (T2WI), renowned for its sensitivity to edema and lesions, faces clinical limitations due to prolonged scanning time, increasing patient discomfort, and motion artifacts. The individual applications of artificial intelligence-assisted compressed sensing (ACS) and deep learning-based reconstruction (DLR) technologies have demonstrated effectiveness in accelerated scanning. However, the synergistic potential of ACS combined with DLR at 5.0T remains unexplored. This study systematically evaluates the diagnostic efficacy of the integrated ACS-DLR technique for T2WI at 5.0T, comparing it to conventional parallel imaging (PI) protocols.

Authors

  • Yun Wen
    Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404000, China.
  • Huan Ma
    School of Public Health, Zhejiang Chinese Medical University, Hangzhou, China.
  • Shaoxin Xiang
    MR Collaboration, United Imaging Research Institute of Intelligent Imaging, Beijing, 100089, China.
  • Zhichao Feng
    Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China.
  • Chuanjiang Guan
    Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404000, China. 843081898@qq.com.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.

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