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Updated: May 3, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

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

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 differentiation Bone quantitative imaging Full 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.