Research Papers

A Feature-Based Morphing Methodology for Computationally Modeled Biological Structures Applied to Left Atrial Fiber Directions

[+] Author and Article Information
Alessandro Satriano

Member of ASME
Graduate Program in Biomedical Engineering,
The University of Calgary,
Calgary, T2N 1N4 Alberta Canada
e-mail: asatrian@ucalgary.ca

Chiara Bellini

Member of ASME
Department of Mechanical and
Manufacturing Engineering,
The University of Calgary,
Calgary, T2N 1N4 Alberta Canada
e-mail: cbellini@ucalgary.ca

Edward J. Vigmond

L'Institut de Rythmologie et
Modélisation Cardiaque,
PTIB - Hôpital Xavier Arnozan,
Université Bordeaux 1,
Pessac, 33604, France
Associate Professor
Department of Electrical and
Computer Engineering,
The University of Calgary,
Calgary, T2N 1N4 Alberta Canada
e-mail: edward.vigmond@u-bordeaux1.fr

Elena S. Di Martino

Assistant Professor
Member of ASME
Department of Civil Engineering,
Centre for Biomedical Engineering
Research and Education,
The University of Calgary,
Calgary, T2N 1N4 Alberta Canada
e-mail: edimarti@ucalgary.ca

1Corresponding author.

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received March 29, 2012; final manuscript received December 17, 2012; accepted manuscript posted January 10, 2013; published online February 11, 2013. Assoc. Editor: Jeffrey W. Holmes.

J Biomech Eng 135(3), 031001 (Feb 11, 2013) (7 pages) Paper No: BIO-12-1122; doi: 10.1115/1.4023369 History: Received March 29, 2012; Revised December 17, 2012; Accepted January 10, 2013

To properly simulate the behavior of biological structures through computer modeling, there exists a need to describe parameters that vary locally. These parameters can be obtained either from literature or from experimental data and they are often assigned to regions in the model as lumped values. Furthermore, parameter values may be obtained on a representative case and may not be available for each specific modeled organ. We describe a semiautomated technique to assign detailed maps of local tissue properties to a computational model of a biological structure. We applied the method to the left atrium of the heart. The orientation of myocytes in the tissue as obtained from histologic analysis was transferred to the 3D model of a porcine left atrium. Finite element method (FEM) dynamic simulations were performed by using an isotropic, neo-Hookean, constitutive model first, then adding an anisotropic, cardiomyocyte oriented, Fung-type component. Results showed higher stresses for the anisotropic material model corresponding to lower stretches in the cardiomyocyte directions. The same methodology can be applied to transfer any map of parameters onto a discretized finite element model.

Copyright © 2013 by ASME
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Fig. 1

Schematics of the developed protocol. Two planar maps, one obtained from imaging modality and one for the tissue properties are obtained. The result of the procedure is a 3D discretized geometry featuring tissue parameter values.

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Fig. 2

Frontal (a) and posterior (b) views of the left atrium. The structure is divided into four substructures: pulmonary veins (red), venous region (brown), appendage (blue), mitral vestibule (green). User-defined curvilinear frontal and posterior axes are visible (dotted red line). Posterior and anterior axes are also selected for each of the pulmonary veins.

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Fig. 3

A simplified description of cardiomyocyte directions for the left atrium as described in [7] is presented in (a). In (b) a representation of the same cardiomyocyte directions is shown over a flat domain. Colors in the images correspond to functionally different substructures.

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Fig. 4

In (a) the intermediate average curvilinear axis is shown. On the right-hand side (b) is the lateral view of the nodes after rectification of the intermediate axis. In (c) a display of the geometry in cylindrical coordinates is provided. By neglecting the radial ρ axis, a planar description of the main body of the left atrium can be obtained.

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Fig. 5

3D result of the assignment of cardiomyocyte directions to a discretized model of the left atrium. Anterior (a) and posterior (b) views are provided.

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Fig. 6

Distribution of average values of dot products between the cardiomyocyte direction of each element and its neighbors. Values closer to 1 indicate parallelism between the cardiomyocyte direction of an element and the fiber orientation of its adjacent elements, whereas 0 indicates a change in cardiomyocyte direction.

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Fig. 7

Maps of first principal stress (in kPa) for the isotropic (top figures) and the fiber-reinforced (bottom figures) material models

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Fig. 8

Distribution of the L2 norm at the instant of pressure peak for the functions Δt and Δλ, defined along the selected myocyte paths on the left atrium

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Fig. 9

Distribution of the absolute value of the dot product between cardiomyocyte directions and the directions of the first principal Cauchy stress for an isotropic, neo-Hookean constitutive model, at peak-diastolic pressure. (a) Frontal view; (b) posterior view.



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