Multi-parameter full-waveform inversion ultrasonic imaging method based on regularization

By introducing the KF dissipative model and the conjugate gradient method, the multi-parameter full waveform inversion method (QFWI) solves the problem of inaccurate reconstruction of traditional ultrasound imaging in complex media, achieves high-resolution reconstruction of sound velocity and quality factor Q, and can accurately distinguish between benign and malignant breast lesions, with good clinical application prospects.

CN120807679AActive Publication Date: 2025-10-17TIANJIN UNIV
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Patent Information

Application Number
CN202510881322.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-27
Publication Date
2025-10-17
Estimated Expiration
2045-06-27

AI Technical Summary

Technical Problem

Traditional single-parameter full waveform inversion ultrasound imaging methods cannot accurately simulate the phenomenon of sound wave propagation when dealing with complex media, resulting in the reconstruction results being unable to effectively distinguish medium characteristics, especially when distinguishing benign and malignant breast lesions.

Method used

A regularized multi-parameter full waveform inversion (QFWI) method is adopted. By introducing the KF dissipation model and the conjugate gradient method, a multi-parameter joint inversion strategy is constructed. Combining the gradient information of the sound velocity and the quality factor Q, a two-stage inversion strategy is adopted to achieve high-precision reconstruction.

Benefits of technology

It effectively overcomes the limitations of traditional methods, achieves high-resolution reconstruction of sound velocity and quality factor Q, can accurately identify the nature of soft tissue lesions in complex tissues, and improves the accuracy of clinical applications.

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Abstract

The invention discloses a regularization-based multi-parameter full-waveform inversion ultrasonic imaging method, which comprises the following steps: implementing full-matrix time domain sound field data acquisition on a to-be-imaged model, and obtaining a frequency domain observation sound field after discrete Fourier transform; constructing a QFWI forward modeling equation to obtain a forward modeling simulation sound field; calculating a data residual error between the frequency domain observation sound field and the forward simulation sound field, and constructing a QFWI target function; further obtaining gradient information of model parameter sound velocity distribution and quality factor Q distribution; calculating the updating direction of the model parameters; and based on the initial sound velocity distribution and the initial quality factor Q distribution of the to-be-imaged model and the gradient information and the updating direction of the model parameters, performing iterative updating on the initial sound velocity distribution and the initial quality factor Q distribution to obtain final sound velocity distribution and final quality factor Q distribution. The method not only accurately simulates the propagation phenomenon of sound waves in a complex medium, but also can effectively distinguish medium characteristics according to a reconstruction result, and has a good clinical application prospect.
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Citation Information

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