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Shear complex modulus imaging utilizing frequency combination in the least mean square/algebraic Helmholtz inversion
Shear complex modulus imaging utilizing frequency combination in the least mean square/algebraic Helmholtz inversion
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Shear complex modulus imaging utilizing frequency combination in the least mean square/algebraic Helmholtz inversion
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Shear complex modulus imaging utilizing frequency combination in the least mean square/algebraic Helmholtz inversion
Shear complex modulus imaging utilizing frequency combination in the least mean square/algebraic Helmholtz inversion
Journal Article

Shear complex modulus imaging utilizing frequency combination in the least mean square/algebraic Helmholtz inversion

2024
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Overview
Complex shear modulus imaging (CSMI) is a technique used to determine the elasticity and viscosity of soft tissues; it aids in investigating tissue structure and detecting tumors. CSMI methods can be categorized into quasi-static and dynamic approaches. The dynamic method utilizes force excitation and particle velocity measurements to estimate the Complex Shear Modulus (CSM). However, noise poses a challenge to shear wave estimation, affecting accuracy. Researchers are actively exploring adaptive filtering techniques and signal processing algorithms to address the noise issue and obtain reliable shear wave estimations. By integrating the Algebraic Helmholtz Inversion (AHI) method and the Least Mean Square (LMS) filter, imaging accuracy can be enhanced by mitigating the impact of noise. This study introduces a pioneering advancement in Complex Shear Modulus Imaging (CSMI). A new approach is proposed, using a dual-frequency excitation technique, strategically employing 100 Hz and 150 Hz frequencies. The proposed method is meticulously designed to enhance the overall efficiency of CSMI. Furthermore, a highly effective signal sampling and spectrum estimation procedure is introduced, aimed at elevating the accuracy of Algebraic Helmholtz Inversion (AHI) and significantly mitigating computational complexity in contrast to prior investigations.