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Proceedings Paper

Locally homogenized and de-noised vector fields for cardiac fiber tracking in DT-MRI images
Author(s): Alireza Akhbardeh; Fijoy Vadakkumpadan; Jason Bayer; Natalia A. Trayanova
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Paper Abstract

In this study we develop a methodology to accurately extract and visualize cardiac microstructure from experimental Diffusion Tensor (DT) data. First, a test model was constructed using an image-based model generation technique on Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. These images were derived from a dataset having 122x122x500 um3 voxel resolution. De-noising and image enhancement was applied to this high-resolution dataset to clearly define anatomical boundaries within the images. The myocardial tissue was segmented from structural images using edge detection, region growing, and level set thresholding. The primary eigenvector of the diffusion tensor for each voxel, which represents the longitudinal direction of the fiber, was calculated to generate a vector field. Then an advanced locally regularizing nonlinear anisotropic filter, termed Perona-Malik (PEM), was used to regularize this vector field to eliminate imaging artifacts inherent to DT-MRI from volume averaging of the tissue with the surrounding medium. Finally, the vector field was streamlined to visualize fibers within the segmented myocardial tissue to compare the results with unfiltered data. With this technique, we were able to recover locally regularized (homogenized) fibers with a high accuracy by applying the PEM regularization technique, particularly on anatomical surfaces where imaging artifacts were most apparent. This approach not only aides in the visualization of noisy complex 3D vector fields obtained from DT-MRI, but also eliminates volume averaging artifacts to provide a realistic cardiac microstructure for use in electrophysiological modeling studies.

Paper Details

Date Published: 13 March 2009
PDF: 10 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72611R (13 March 2009); doi: 10.1117/12.811629
Show Author Affiliations
Alireza Akhbardeh, The Johns Hopkins Univ. (United States)
Fijoy Vadakkumpadan, The Johns Hopkins Univ. (United States)
Jason Bayer, The Johns Hopkins Univ. (United States)
Natalia A. Trayanova, The Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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