Shear modulus decomposition algorithm in magnetic resonance elastography

Oh In Kwon; Chunjae Park; Hyun Soo Nam; Eung Je Woo; Jin Keun Seo; K. J. Glaser; A. Manduca; R. L. Ehman

doi:10.1109/TMI.2009.2019823

Shear modulus decomposition algorithm in magnetic resonance elastography

Oh In Kwon, Chunjae Park, Hyun Soo Nam, Eung Je Woo, Jin Keun Seo, K. J. Glaser, A. Manduca, R. L. Ehman

Department of Computational Science and Engineering

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

Magnetic resonance elastography (MRE) is an imaging modality capable of visualizing the elastic properties of an object using magnetic resonance imaging (MRI) measurements of transverse acoustic strain waves induced in the object by a harmonically oscillating mechanical vibration. Various algorithms have been designed to determine the mechanical properties of the object under the assumptions of linear elasticity, isotropic and local homogeneity. One of the challenging problems in MRE is to reduce the noise effects and to maintain contrast in the reconstructed shear modulus images. In this paper, we propose a new algorithm designed to reduce the degree of noise amplification in the reconstructed shear modulus images without the assumption of local homogeneity. Investigating the relation between the measured displacement data and the stress wave vector, the proposed algorithm uses an iterative reconstruction formula based on a decomposition of the stress wave vector. Numerical simulation experiments and real experiments with agarose gel phantoms and human liver data demonstrate that the proposed algorithm is more robust to noise compared to standard inversion algorithms and stably determines the shear modulus.

Original language	English
Article number	5265288
Pages (from-to)	1526-1533
Number of pages	8
Journal	IEEE Transactions on Medical Imaging
Volume	28
Issue number	10
DOIs	https://doi.org/10.1109/TMI.2009.2019823
Publication status	Published - 2009 Oct

Bibliographical note

Funding Information:
Manuscript received March 08, 2009; revised March 18, 2009. Current version published September 25, 2009. The work of O. I. Kwon was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2005-201-C00004), This work was supported in part by the SRC/ERC program of MOST/KOSEF (R11-2002-103) and in part by the National Institutes of Health under Grant EB001981 and Grant NIH-C06-RR018898. Asterisk indicates corresponding author. *O. I. Kwon is with the Department of Mathematics, Konkuk University, Seoul 143–701, Korea (e-mail: oikwon@konkuk.ac.kr).

All Science Journal Classification (ASJC) codes

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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title = "Shear modulus decomposition algorithm in magnetic resonance elastography",

abstract = "Magnetic resonance elastography (MRE) is an imaging modality capable of visualizing the elastic properties of an object using magnetic resonance imaging (MRI) measurements of transverse acoustic strain waves induced in the object by a harmonically oscillating mechanical vibration. Various algorithms have been designed to determine the mechanical properties of the object under the assumptions of linear elasticity, isotropic and local homogeneity. One of the challenging problems in MRE is to reduce the noise effects and to maintain contrast in the reconstructed shear modulus images. In this paper, we propose a new algorithm designed to reduce the degree of noise amplification in the reconstructed shear modulus images without the assumption of local homogeneity. Investigating the relation between the measured displacement data and the stress wave vector, the proposed algorithm uses an iterative reconstruction formula based on a decomposition of the stress wave vector. Numerical simulation experiments and real experiments with agarose gel phantoms and human liver data demonstrate that the proposed algorithm is more robust to noise compared to standard inversion algorithms and stably determines the shear modulus.",

author = "Kwon, {Oh In} and Chunjae Park and Nam, {Hyun Soo} and Woo, {Eung Je} and Seo, {Jin Keun} and Glaser, {K. J.} and A. Manduca and Ehman, {R. L.}",

note = "Funding Information: Manuscript received March 08, 2009; revised March 18, 2009. Current version published September 25, 2009. The work of O. I. Kwon was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD) (KRF-2005-201-C00004), This work was supported in part by the SRC/ERC program of MOST/KOSEF (R11-2002-103) and in part by the National Institutes of Health under Grant EB001981 and Grant NIH-C06-RR018898. Asterisk indicates corresponding author. *O. I. Kwon is with the Department of Mathematics, Konkuk University, Seoul 143–701, Korea (e-mail: oikwon@konkuk.ac.kr).",

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AU - Kwon, Oh In

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AU - Glaser, K. J.

AU - Manduca, A.

AU - Ehman, R. L.

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N2 - Magnetic resonance elastography (MRE) is an imaging modality capable of visualizing the elastic properties of an object using magnetic resonance imaging (MRI) measurements of transverse acoustic strain waves induced in the object by a harmonically oscillating mechanical vibration. Various algorithms have been designed to determine the mechanical properties of the object under the assumptions of linear elasticity, isotropic and local homogeneity. One of the challenging problems in MRE is to reduce the noise effects and to maintain contrast in the reconstructed shear modulus images. In this paper, we propose a new algorithm designed to reduce the degree of noise amplification in the reconstructed shear modulus images without the assumption of local homogeneity. Investigating the relation between the measured displacement data and the stress wave vector, the proposed algorithm uses an iterative reconstruction formula based on a decomposition of the stress wave vector. Numerical simulation experiments and real experiments with agarose gel phantoms and human liver data demonstrate that the proposed algorithm is more robust to noise compared to standard inversion algorithms and stably determines the shear modulus.

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Shear modulus decomposition algorithm in magnetic resonance elastography

Abstract

Bibliographical note

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