alexa Assessment of Myocardial Function from Cine Cardiac MRI
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Assessment of Myocardial Function from Cine Cardiac MRI Using a Novel 4D Tracking Approach

Hisham Sliman1,2, Ahmed Elnakib1,3, Garth M Beache4, Adel Elmaghraby2 and Ayman El-Baz1*

1Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, USA

2Computer Engineering and Computer Science Department, University of Louisville, Louisville, USA

3Electronics and Communications Engineering, Mansoura University, Mansoura, Egypt

4Department of Radiology, University of Louisville, Louisville, USA

*Corresponding Author:
Ayman El-Baz
Bioimaging Laboratory
Bioengineering Department
University of Louisville, Louisville
KY 40292, USA
Tel: 502-852-5092
Fax: 502-852-6806
E-mail: [email protected]

Received Date: June 03, 2014; Accepted Date: June 25, 2014; Published Date: June 27, 2014

Citation: Sliman H, Elnakib A, Beache GM, Elmaghraby A, El-Baz A (2014) Assessment of Myocardial Function from Cine Cardiac MRI Using a Novel 4D Tracking Approach. J Comput Sci Syst Biol 7:169-173. doi:10.4172/jcsb.1000152

Copyright: © 2014 Sliman H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

A novel approach for assessing the myocardial function using 4D cine cardiac magnetic resonance imaging (CMRI) is proposed. The myocardial function is assessed by estimating the left ventricle (LV) wall thickening based on a novel 4D tracking approach that tracks the LV wall during the cardiac cycle. Initially, the proposed 4D approach tracks the surface points on the LV wall by solving a 3D Laplace equation between each two subsequent LV surfaces. In order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process, the initial locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model. Using the iteratively adjusted solution of the 3D Laplace equation, the myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall. Experimental results on in vivo data demonstrate that our approach outperforms 2D wall thickening estimation approaches.

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