Adaptive mesh refinement for elastic modulus reconstruction in elastography

Author(s):  
Wenxia Wang ◽  
Wei Zou ◽  
Danfeng Hu ◽  
Jiajun Wang

Meshes play a crucial role in determining the accuracy of the elastic modulus reconstruction in the elastography when the finite element method is employed. In this article, we propose an adaptive mesh refinement strategy which can ensure the coincidence of the meshes with the shape of the inclusions in the observed tissue. This strategy is based on the intensity distribution of the strain image where the variance of the strain distribution in each element of the meshes is used to measure the homogeneity of the element, that is, the larger the strain variance is the more inhomogeneous the element will be and hence more detailed information will be included in this element. For more accurate reconstruction of such detailed information, mesh refinement procedure is implemented in such elements. Besides, two refinement steps are employed for the reconstruction to improve the fitness of the reconstructed image and the observed tissue. Simulation results show that the two-stage adaptive mesh refinement algorithm performs well without needing any prior information about the internal geometric shape in tissue. Not only Young’s moduli of models but also shapes of the inclusions can be reconstructed perfectly and quickly with our proposed method.

2006 ◽  
Vol 196 (1) ◽  
pp. 115-131 ◽  
Author(s):  
Denise Burgarelli ◽  
Mauricio Kischinhevsky ◽  
Rodney Josué Biezuner

Author(s):  
Fred van Keulen ◽  
Vassili Toropov ◽  
Valery Markine

Abstract Application of the Multi-point Approximation Method (MAM) to structural optimization is considered. Structural analyses are performed by means of the finite element method with Adaptive Mesh Refinement (AMR). The required discretization errors are changed during the optimization process to achieve a higher computational efficiency. A straightforward combination of the MAM and AMR may yield complications, which are discussed in detail. Therefore, several modifications in the MAM are necessary. An alternative strategy for determining the explicit approximation functions using a weighted least-squares fitting is proposed. The applied weight coefficients reflect the levels of the discretization errors. The approximation functions are fitted with a sub-set of the available structural response analyses. An alternative move limit strategy is given. On the basis of several numerical examples it is shown that the proposed modifications improve the convergence characteristics of the MAM when combined with AMR. Moreover it is demonstrated that the proposed refinements are also beneficial for optimization of systems with noisy objective and constraint functions.


2011 ◽  
Vol 16 (3) ◽  
pp. 577-592 ◽  
Author(s):  
George Shu Heng Pau ◽  
John B. Bell ◽  
Ann S. Almgren ◽  
Kirsten M. Fagnan ◽  
Michael J. Lijewski

1997 ◽  
Author(s):  
J.S. Saltzman ◽  
D.L. Brown ◽  
K.D. Brislawn ◽  
G.S. Chesshire ◽  
D.J. Quinlan ◽  
...  

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