A multi-task neural network for full waveform ultrasonic bone imaging.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 25, 2025

Abstract

BACKGROUND AND OBJECTIVE: It is a challenging task to use ultrasound for bone imaging, as the bone tissue has a complex structure with high acoustic impedance and speed-of-sound (SOS). Recently, full waveform inversion (FWI) has shown promising imaging for musculoskeletal tissues. However, the FWI showed a limited ability and tended to produce artifacts in bone imaging because the inversion process would be more easily trapped in local minimum for bone tissue with a large discrepancy in SOS distribution between bony and soft tissues. In addition, the application of FWI required a high computational burden and relatively long iterations. The objective of this study was to achieve high-resolution ultrasonic imaging of bone using a deep learning-based FWI approach.

Authors

  • Peiwen Li
    Department of Radiology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.
  • Tianyu Liu
    Department of Automation, Tsinghua University,Beijing, China.
  • Heyu Ma
    Institute of Biomedical Engineering & Technology, Academy for Engineering and Technology, Fudan University, Shanghai 200438, 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.
  • Dean Ta

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