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Proceedings Paper

A method for myocardial contraction force reconstruction for tissue viability assessment
Author(s): Cristian A. Linte; Terry M. Peters; Abbas Samani
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Paper Abstract

Myocardial infarction results in myocardial necrosis, usually caused by an imbalance in the oxygen supply and demand to myocardial tissue. To our knowledge there is no technique that can provide quantitative direct information concerning the intensity, extent and location of the infarction. Contraction forces generated by cardiac tissues represent a quantitative and direct measure of the myocardial functionality, since it is expected that infarcted tissue generate little or no contraction force. Our objective is to develop a biomechanics based reconstruction technique to image myocardial contraction forces, for the purpose of assessing the viability of cardiac tissues. This technique is designed to reconstruct the contraction forces by inverting myocardial tissue displacement data acquired throughout heart beat cycles using conventional imaging techniques. Recognizing that myocardial contraction force distribution is 3D, we assumed an axisymmetric myocardial geometry to demonstrate the concept. With this assumption, the inversion algorithm was developed and implemented in 2D space. As a preliminary analysis, a simulation involving a 2D representation of myocardial wall tissue was carried out. The tissue was modeled as a homogeneous material with isotropic and linear elastic material properties. Assuming an axisymmetric contraction force distribution, a finite element analysis was performed on the tissue model, and a 2D displacement field was generated. The developed inversion algorithm was then employed to reconstruct the force distribution, which was ultimately compared to the original force field. The reconstruction error, estimated as the difference between the two force fields and normalized by the magnitude of the reference distribution, averaged to +/-10%. Results demonstrate that our myocardial contraction force reconstruction algorithm is reasonably accurate and robust.

Paper Details

Date Published: 13 March 2006
PDF: 10 pages
Proc. SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, 61432O (13 March 2006); doi: 10.1117/12.655421
Show Author Affiliations
Cristian A. Linte, Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Terry M. Peters, Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Abbas Samani, Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)

Published in SPIE Proceedings Vol. 6143:
Medical Imaging 2006: Physiology, Function, and Structure from Medical Images
Armando Manduca; Amir A. Amini, Editor(s)

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