Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study
Elasticity and viscosity of soft tissues can be obtained from the complex shear modulus imaging (CSMI). CSMI is often used not only to investigate the structure of tissues but also to detect tumors in tissues. One of the most popular ways to categorize the methods used in CSMI is into quasi-static a...
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doaj-29ac8e6a276d46068b584c6442ef1f982021-06-21T01:35:23ZengAIMS PressMathematical Biosciences and Engineering1551-00182020-01-0117140441710.3934/mbe.2020022Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation studyThu-Ha Pham-Thi0Quang-Hai luong1Van-Dung Nguyen2Duc-Tan Tran3Huu-Tue Huynh41. Hanoi National University of Education, Vietnam2. Faculty of Control Engineering, Le Quy Don technical University, 236 Hoang Quoc Viet, Bac Tu Liem, Hanoi 11313, Vietnam3. Nguyen Tat Thanh University, Vietnam4. Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, Vietnam 5. Phenikaa Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC, No.167 Hoang Ngan, Trung Hoa, Cau Giay, Hanoi 11313, Vietnam6. International University, HCM Vietnam National University, VietnamElasticity and viscosity of soft tissues can be obtained from the complex shear modulus imaging (CSMI). CSMI is often used not only to investigate the structure of tissues but also to detect tumors in tissues. One of the most popular ways to categorize the methods used in CSMI is into quasi-static and dynamic methods. In the dynamic method, a force excitation is used to create the shear wave propagation, and the particle velocities are measured to extract their amplitude and phase at spatial locations. These parameters are then employed to directly or indirectly estimate the Complex Shear Modulus (CSM) represented by elasticity and viscosity. Algebraic Helmholtz Inversion (AHI) algorithm provides the direct estimation of CSM using the Finite Difference Time Domain (FDTD) technique. The limitation of this method, however, is that the noise generated from measuring the particle velocity strongly degrades the accuracy of the estimation. To overcome this problem, we proposed in this paper an adaptive AHI (AAHI) algorithm that offers a good performance in CSMI with a mean error of 2.06%.https://www.aimspress.com/article/doi/10.3934/mbe.2020022?viewType=HTMLshear waveelasticityviscositycsm estimationleast mean squarealgebraic helmholtz inversion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Thu-Ha Pham-Thi Quang-Hai luong Van-Dung Nguyen Duc-Tan Tran Huu-Tue Huynh |
spellingShingle |
Thu-Ha Pham-Thi Quang-Hai luong Van-Dung Nguyen Duc-Tan Tran Huu-Tue Huynh Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study Mathematical Biosciences and Engineering shear wave elasticity viscosity csm estimation least mean square algebraic helmholtz inversion |
author_facet |
Thu-Ha Pham-Thi Quang-Hai luong Van-Dung Nguyen Duc-Tan Tran Huu-Tue Huynh |
author_sort |
Thu-Ha Pham-Thi |
title |
Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study |
title_short |
Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study |
title_full |
Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study |
title_fullStr |
Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study |
title_full_unstemmed |
Two-dimensional complex shear modulus imaging of soft tissues by integration of Algebraic Helmoltz Inversion and LMS filter into dealing with noisy data: a simulation study |
title_sort |
two-dimensional complex shear modulus imaging of soft tissues by integration of algebraic helmoltz inversion and lms filter into dealing with noisy data: a simulation study |
publisher |
AIMS Press |
series |
Mathematical Biosciences and Engineering |
issn |
1551-0018 |
publishDate |
2020-01-01 |
description |
Elasticity and viscosity of soft tissues can be obtained from the complex shear modulus imaging (CSMI). CSMI is often used not only to investigate the structure of tissues but also to detect tumors in tissues. One of the most popular ways to categorize the methods used in CSMI is into quasi-static and dynamic methods. In the dynamic method, a force excitation is used to create the shear wave propagation, and the particle velocities are measured to extract their amplitude and phase at spatial locations. These parameters are then employed to directly or indirectly estimate the Complex Shear Modulus (CSM) represented by elasticity and viscosity. Algebraic Helmholtz Inversion (AHI) algorithm provides the direct estimation of CSM using the Finite Difference Time Domain (FDTD) technique. The limitation of this method, however, is that the noise generated from measuring the particle velocity strongly degrades the accuracy of the estimation. To overcome this problem, we proposed in this paper an adaptive AHI (AAHI) algorithm that offers a good performance in CSMI with a mean error of 2.06%. |
topic |
shear wave elasticity viscosity csm estimation least mean square algebraic helmholtz inversion |
url |
https://www.aimspress.com/article/doi/10.3934/mbe.2020022?viewType=HTML |
work_keys_str_mv |
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