Diffusion tensor MR image data gives at each voxel in the image a symmetric, positive definite matrix that is
denoted as the diffusion tensor at that voxel location. The eigenvectors of the tensor represent the principal
directions of anisotopy in water diffusion. The eigenvector with the largest eigenvalue indicates the local orientation
of tissue fibers in 3D as water is expected to diffuse preferentially up and down along the fiber tracts.
Although there is no anatomically valid positive or negative direction to these fiber tracts, for many applications,
it is of interest to assign an artificial direction to the fiber tract by choosing one of the two signs of the principal
eigenvector in such a way that in local neighborhoods the assigned directions are consistent and vary smoothly
We demonstrate here an algorithm for realigning the principal eigenvectors by flipping their sign such that it
assigns a locally consistent and spatially smooth fiber direction to the eigenvector field based on a Monte-Carlo
algorithm adapted from updating clusters of spin systems. We present results that show the success of this
algorithm on 11 available unsegmented canine cardiac volumes of both healthy and failing hearts.
A method for measuring the thickness of the ventricular heart wall from 3D MRI images is presented. The quantification of thickness could be useful clinically to measure the health of the heart muscle. The method involves extending a Laplace-equation-based definition of thickness between two surfaces to the ventricular heart wall geometry. Based on the functional organization of the heart, it is proposed that the heart be segmented into two volumes, the left ventricular wall which completely encloses the left ventricle and the right ventricular wall which attaches to the left ventricular wall to enclose the right ventricle, and that the thickness of these two volumes be calculated separately. An algorithm for performing this segmentation automatically is presented. The results of the automatic segmentation algorithm were compared to the results of manual segmentations of both normal and failing hearts and an average of 99.28% of ventricular wall voxels were assigned the same label in both the automatic and the manual segmentations. The thickness of eleven hearts, seven normal and four failing was measured.