DocumentCode :
74678
Title :
Mesh Adaptation for Improving Elasticity Reconstruction Using the FEM Inverse Problem
Author :
Goksel, O. ; Eskandari, Hani ; Salcudean, Septimiu E.
Author_Institution :
Comput. Vision Lab., Swiss Fed. Inst. of Technol. (ETH), Zürich, Switzerland
Volume :
32
Issue :
2
fYear :
2013
fDate :
Feb. 2013
Firstpage :
408
Lastpage :
418
Abstract :
The finite element method is commonly used to model tissue deformation in order to solve for unknown parameters in the inverse problem of viscoelasticity. Typically, a (regular-grid) structured mesh is used since the internal geometry of the domain to be identified is not known a priori. In this work, the generation of problem-specific meshes is studied and such meshes are shown to significantly improve inverse-problem elastic parameter reconstruction. Improved meshes are generated from axial strain images, which provide an approximation to the underlying structure, using an optimization-based mesh adaptation approach. Such strain-based adapted meshes fit the underlying geometry even at coarse mesh resolutions, therefore improving the effective resolution of the reconstruction at a given mesh size/complexity. Elasticity reconstructions are then performed iteratively using the reflective trust-region method for optimizing the fit between estimated and observed displacements. This approach is studied for Young´s modulus reconstruction at various mesh resolutions through simulations, yielding 40%-72% decrease in root-mean-square reconstruction error and 4-52 times improvement in contrast-to-noise ratio in simulations of a numerical phantom with a circular inclusion. A noise study indicates that conventional structured meshes with no noise perform considerably worse than the proposed adapted meshes with noise levels up to 20% of the compression amplitude. A phantom study and preliminary in vivo results from a breast tumor case confirm the benefit of the proposed technique. Not only conventional axial strain images but also other elasticity approximations can be used to adapt meshes. This is demonstrated on images generated by combining axial strain and axial-shear strain, which enhances lateral image contrast in particular settings, consequently further improving mesh-adapted reconstructions.
Keywords :
Young´s modulus; biomechanics; biomedical MRI; compressibility; computerised tomography; elastic deformation; finite element analysis; image enhancement; image reconstruction; inverse problems; medical image processing; optimisation; phantoms; shear deformation; tumours; viscoelasticity; FEM inverse problem; Young´s modulus reconstruction; axial strain images; axial-shear strain; breast tumor case; circular inclusion; coarse mesh resolutions; compression amplitude; computerised tomography; contrast-to-noise ratio; conventional axial strain images; conventional structured meshes; finite element method; image generation; internal domain geometry; inverse-problem elasticity parameter reconstruction; lateral image contrast enhancement; magnetic resonance imaging; mesh size-complexity; model tissue deformation; noise study; numerical phantom; optimization-based mesh adaptation approach; problem-specific meshes; reflective trust-region method; regular-grid structured mesh adaptation; root-mean-square reconstruction error; strain-based adapted meshes; viscoelasticity; Adaptation models; Approximation methods; Elasticity; Finite element methods; IP networks; Image reconstruction; Strain; Patient-specific modeling; strain-based improvement of elastic parameter reconstruction; trust-region method; Algorithms; Computer Simulation; Elastic Modulus; Elasticity Imaging Techniques; Finite Element Analysis; Image Enhancement; Image Interpretation, Computer-Assisted; Models, Biological; Reproducibility of Results; Sensitivity and Specificity; Viscosity;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2012.2228664
Filename :
6359951
Link To Document :
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