Ultrasound shear wave phase velocity imaging using black-box system identification (BSI): a data-driven approach

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Acoustics

Background:

• Shear wave elasticity imaging (SWEI) is crucial for diagnosing tissue lesions.
• Traditional SWEI struggles with small targets (<10 mm) due to insufficient shear wavelength resolution.
• High-frequency content is key for improving accuracy in small-target reconstruction.

Purpose of the Study:

• Introduce a novel phase velocity (PV) estimation technique.
• Utilize short shear wavelengths for enhanced accuracy in SWEI.
• Compare the proposed PV method with conventional group velocity (GV) and existing PV techniques.

Main Methods:

• Developed a black-box system identification (BSI) model for shear wave signal transition.
• Applied BSI for local phase velocity (PV) estimation.
• Validated BSI through numerical dispersion measurements and phantom imaging, comparing it with local wavenumber estimation (LWE).

Main Results:

• BSI demonstrated high accuracy in dispersion estimation across simulated viscoelastic media.
• Both PV methods (BSI and LWE) showed good agreement and superior target quantification compared to GV.
• BSI produced artifact-free images, unlike LWE which was affected by noise.

Conclusions:

• Phase velocity (PV) modality offers superior target visualization over group velocity (GV) by enabling higher imaging frequencies.
• The proposed black-box system identification (BSI) technique is effective for accurate PV estimation in SWEI.
• Data-driven modeling shows promise for advanced soft tissue characterization.