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Two-dimensional viscoelastic imaging using an enhanced FDTD-AHI approach

Two-dimensional viscoelastic imaging using an enhanced FDTD-AHI approach PurposeUltrasound viscoelastography is a quantitative and convenient imaging model that images soft tissues based on shear waves’ propagation. Thanks to the interaction between ultrasonic waves and shear waves, the physical features of soft tissues (elasticity, viscosity) are expressed by complex shear modulus (CSM) parameters, useful information for diagnosing tissue medical states. Therefore, this paper presented a new efficient approach to estimate CSM and reconstruct the viscoelastic images.MethodsThis paper develops a two-dimensional shear wave propagation model applying finite difference time domain (FDTD) technique and then employing a proposed bandpass filter to reduce measured particle velocity noise of shear wave; finally, algebraic Helmholtz inversion (AHI) is utilized to estimate the CSM directly. As already known, excitation frequency for ultrasound viscoelastography system is a single one. In the previous research, the low pass filter is used to reduce frequency vibrations and measurement noise as well as fluctuations at low frequency; however, it is unable to completely eliminate noise since the measured particle velocity is affected by high-frequency noise and fluctuations in low frequency. Therefore, the enhanced FDTD-AHI algorithm applies a bandpass filter, which retains only the frequency band containing the excitation frequency, promising to eliminate low- and high-frequency noise. Moreover, we proposed to cut the transient part of the filtered signal to avoid affecting AHI step and thus to improve the CSM estimation.ResultsThe simulation scenarios have proved the effectiveness of the enhanced FDTD-AHI algorithm. The proposed method’s normalized error is significantly reduced compared to the traditional manner using a low pass filter. We reconstructed two-dimensional images of elasticity and viscosity in the area of interest. The estimated quality near the excitation source is quite good. Still, far away from the excitation source, it is of low quality, and there are many large fluctuations due to the reduced shear wave and being much affected by noise. Another limitation of our proposed method is that it can reduce the additive noise in the lower and higher frequencies of the exciting one except the noise around this frequency.ConclusionsThe shear wave propagation model using FDTD method provides high accuracy and low complexity compared to the finite element method (FEM). The absence of optimal or adapted filters in our approach can reduce the imaging system’s computational complexity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Two-dimensional viscoelastic imaging using an enhanced FDTD-AHI approach

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References (27)

Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2021
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-021-00130-x
Publisher site
See Article on Publisher Site

Abstract

PurposeUltrasound viscoelastography is a quantitative and convenient imaging model that images soft tissues based on shear waves’ propagation. Thanks to the interaction between ultrasonic waves and shear waves, the physical features of soft tissues (elasticity, viscosity) are expressed by complex shear modulus (CSM) parameters, useful information for diagnosing tissue medical states. Therefore, this paper presented a new efficient approach to estimate CSM and reconstruct the viscoelastic images.MethodsThis paper develops a two-dimensional shear wave propagation model applying finite difference time domain (FDTD) technique and then employing a proposed bandpass filter to reduce measured particle velocity noise of shear wave; finally, algebraic Helmholtz inversion (AHI) is utilized to estimate the CSM directly. As already known, excitation frequency for ultrasound viscoelastography system is a single one. In the previous research, the low pass filter is used to reduce frequency vibrations and measurement noise as well as fluctuations at low frequency; however, it is unable to completely eliminate noise since the measured particle velocity is affected by high-frequency noise and fluctuations in low frequency. Therefore, the enhanced FDTD-AHI algorithm applies a bandpass filter, which retains only the frequency band containing the excitation frequency, promising to eliminate low- and high-frequency noise. Moreover, we proposed to cut the transient part of the filtered signal to avoid affecting AHI step and thus to improve the CSM estimation.ResultsThe simulation scenarios have proved the effectiveness of the enhanced FDTD-AHI algorithm. The proposed method’s normalized error is significantly reduced compared to the traditional manner using a low pass filter. We reconstructed two-dimensional images of elasticity and viscosity in the area of interest. The estimated quality near the excitation source is quite good. Still, far away from the excitation source, it is of low quality, and there are many large fluctuations due to the reduced shear wave and being much affected by noise. Another limitation of our proposed method is that it can reduce the additive noise in the lower and higher frequencies of the exciting one except the noise around this frequency.ConclusionsThe shear wave propagation model using FDTD method provides high accuracy and low complexity compared to the finite element method (FEM). The absence of optimal or adapted filters in our approach can reduce the imaging system’s computational complexity.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2021

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