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  3. Rnn-based Full Waveform Inversion For Robust Multi-parameter Bone Quantitative Imaging.
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  3. Rnn-based Full Waveform Inversion For Robust Multi-parameter Bone Quantitative Imaging.

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

Jingyi Xiao1, Dan Li1, Chengcheng Liu2

  • 1School of Information Science and Technology, Fudan University, Shanghai 200433, China.

Computer Methods and Programs in Biomedicine
|May 6, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new recurrent neural network multi-parameter time-domain full waveform inversion (RNN-MPTDFWI) algorithm improves bone quantitative imaging accuracy. This method significantly reduces errors and enhances robustness against transducer position variations compared to frequency-domain full waveform inversion (FDFWI).

Keywords:
Automatic differentiationBone quantitative imagingFull waveform inversion (FWI)Recurrent neural network (RNN)

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Area of Science:

  • Biomedical Imaging
  • Quantitative Bone Imaging
  • Medical Ultrasound

Background:

  • Full waveform inversion (FWI) is crucial for quantitative bone imaging.
  • Frequency-domain FWI (FDFWI) is sensitive to transducer position errors, impacting imaging performance.
  • Existing methods require precise transducer placement for accurate results.

Purpose of the Study:

  • To introduce a robust multi-parameter time-domain full waveform inversion algorithm based on a recurrent neural network (RNN-MPTDFWI) for bone quantitative imaging.
  • To overcome the limitations of FDFWI regarding transducer position sensitivity.
  • To improve the accuracy and reliability of bone imaging.

Main Methods:

  • A variable-density acoustic wave equation incorporating sound velocity and bone density is solved within RNN cells as the forward model.
  • Multiscale inversion is performed iteratively using filtered signals from low to high frequencies.
  • Optimization utilizes automatic differentiation for gradient calculation and the Adam algorithm.

    • Main Results:

    • The proposed RNN-MPTDFWI algorithm achieved significant reductions in mean relative errors (MREs) for reconstructed velocity (at least 54.56%) and density (at least 71.64%) compared to FDFWI.
    • Numerical simulations validated the superior performance of RNN-MPTDFWI.
    • The method demonstrated enhanced accuracy in bone parameter reconstruction.

    Conclusions:

    • RNN-MPTDFWI provides more accurate representations of bone geometry and microarchitecture.
    • The algorithm exhibits enhanced robustness against transducer position errors, a key limitation of FDFWI.
    • This advancement offers improved quantitative bone imaging capabilities.