Magnetic resonance image denoising for Rician noise using a novel hybrid transformer-CNN network (HTC-net) and self-supervised pretraining.

Journal: Medical physics
Published Date: Dec 6, 2024

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While deep learning (DL) methods have shown promise for MR image denoising, most of them fail to adequately utilize the long-range information which is important to improve denoising performance. Furthermore, the sample size of paired noisy and noise-free MR images also limits denoising performance.

Authors

  • Shengjie Shi
    Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
  • Cheng Wang
    Department of Pathology, Dalhousie University, Halifax, NS, Canada.
  • Sa Xiao
    Department of Ophthalmology, University of Washington, Seattle, Washington, USA.
  • Haidong Li
    State Key Laboratory of Fine Chemicals , Dalian University of Technology , 2 Linggong Road , Dalian 116024 , P. R. China . Email: fanjl@dlut.edu.cn.
  • Xiuchao Zhao
    Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, People's Republic of China.
  • Fumin Guo
    School of Mechatronics Engineering, Nanchang University, Nanchang 330031, China.
  • Lei Shi
  • Xin Zhou
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.

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