Literature DB >> 17584521

Boundary element method-based regularization for recovering of LV deformation.

Ping Yan1, Albert Sinusas, James S Duncan.   

Abstract

The quantification of left ventricular (LV) deformation from noninvasive image sequences is an important clinical problem. To date, feature information from either magnetic resonance (MR), computed tomographic (CT) or echocardiographic image data have been assembled with the help of different regularization models to estimate LV deformation. The currently available regularization models have tradeoffs related to accuracy, lattice density, physical plausibility and computation time. This paper introduces a new regularization model based on the boundary element method (BEM) which can overcome these tradeoffs. We then employ this new regularization model with the generalized robust point matching (GRPM) strategy to estimate the dense displacement fields and strains from 3D LV image sequences. The approach is evaluated on in vivo cardiac magnetic resonance image sequences. All results are compared to displacements found using implanted markers, taken to be a gold standard. The approach is also evaluated on the 4D real time echocardiographic image sequences and the results demonstrate that the approach is capable of tracking the LV deformation for echocardiography.

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Year:  2007        PMID: 17584521     DOI: 10.1016/j.media.2007.04.007

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  7 in total

1.  A Meshfree Representation for Cardiac Medical Image Computing.

Authors:  Heye Zhang; Zhifan Gao; Lin Xu; Xingjian Yu; Ken C L Wong; Huafeng Liu; Ling Zhuang; Pengcheng Shi
Journal:  IEEE J Transl Eng Health Med       Date:  2018-01-18       Impact factor: 3.316

Review 2.  Machine learning and radiology.

Authors:  Shijun Wang; Ronald M Summers
Journal:  Med Image Anal       Date:  2012-02-23       Impact factor: 8.545

3.  Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis.

Authors:  Nripesh Parajuli; Allen Lu; Kevinminh Ta; John Stendahl; Nabil Boutagy; Imran Alkhalil; Melissa Eberle; Geng-Shi Jeng; Maria Zontak; Matthew O'Donnell; Albert J Sinusas; James S Duncan
Journal:  Med Image Anal       Date:  2019-04-18       Impact factor: 8.545

4.  Contour tracking in echocardiographic sequences via sparse representation and dictionary learning.

Authors:  Xiaojie Huang; Donald P Dione; Colin B Compas; Xenophon Papademetris; Ben A Lin; Alda Bregasi; Albert J Sinusas; Lawrence H Staib; James S Duncan
Journal:  Med Image Anal       Date:  2013-11-06       Impact factor: 8.545

5.  Deformable models with sparsity constraints for cardiac motion analysis.

Authors:  Yang Yu; Shaoting Zhang; Kang Li; Dimitris Metaxas; Leon Axel
Journal:  Med Image Anal       Date:  2014-03-27       Impact factor: 8.545

6.  Radial basis functions for combining shape and speckle tracking in 4D echocardiography.

Authors:  Colin B Compas; Emily Y Wong; Xiaojie Huang; Smita Sampath; Ben A Lin; Prasanta Pal; Xenophon Papademetris; Karl Thiele; Donald P Dione; Mitchel Stacy; Lawrence H Staib; Albert J Sinusas; Matthew O'Donnell; James S Duncan
Journal:  IEEE Trans Med Imaging       Date:  2014-06       Impact factor: 10.048

7.  Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models.

Authors:  Ting Chen; Xiaoxu Wang; Sohae Chung; Dimitris Metaxas; Leon Axel
Journal:  IEEE Trans Med Imaging       Date:  2009-04-14       Impact factor: 10.048

  7 in total

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