RNN-Based Full Waveform Inversion for Robust Multi-Parameter Bone Quantitative Imaging.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 30, 2025

Abstract

BACKGROUND AND OBJECTIVE: The full waveform inversion (FWI) method plays a significant role in bone quantitative imaging. It is shown that even a small deviation in transducer positions can lead to a considerable variation in frequency-domain signals, and result in a marked decline in the performance of frequency-domain full waveform inversion (FDFWI). To address this limitation, a multi-parameter time-domain full waveform inversion algorithm based on a recurrent neural network (RNN-MPTDFWI) is proposed for bone quantitative imaging.

Authors

  • Jingyi Xiao
    School of Information Science and Technology, Fudan University, Shanghai 200433, China.
  • Dan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China.
  • Chengcheng Liu
    State Key Laboratory of Oral Diseases, Department of Periodontics, National Clinical Research Center for Oral Diseases, West China School & Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Jianqiu Zhang
    Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA.
  • Dean Ta

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