Title :
Elastic matching of diffusion tensor MRIs
Author :
Alexander, Daniel ; Gee, James ; Bajcsy, Ruzena
Author_Institution :
Dept. of Radiol., Pennsylvania Univ., Philadelphia, PA, USA
Abstract :
In this paper we discuss work on the use of diffusion tensor MRIs for inter-subject brain matching. A multiresolution elastic matching algorithm for spatial normalisation of 3D image data, has been adapted for use with diffusion tensor data. The hope is that by exploitation of the added information contained in the diffusion tensor image, improved anatomical matches can be found, particularly in white matter regions of the brain. Results show that by matching on the diffusion tensor alone, anisotropic regions of the brain (white matter) are aligned better than if the match is computed on standard structural data. However, there is a cost of some accuracy in the alignment of prominent features in more conventional, structural MRI data, such as PD-, TI- and T2-weighted imagery. If both types of data to drive the matching process, prominent features in both images can be aligned simultaneously. The motivation for this work lies in the characterisation of the distribution of brain images taken from population groups
Keywords :
biomedical MRI; image matching; 3D image data; anatomical matches; diffusion tensor MRIs; elastic matching; inter-subject brain matching; spatial normalisation; white matter regions; Anisotropic magnetoresistance; Brain; Computational Intelligence Society; Costs; Diffusion tensor imaging; Image resolution; Magnetic resonance imaging; Radiology; Spatial resolution; Tensile stress;
Conference_Titel :
Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Conference_Location :
Fort Collins, CO
Print_ISBN :
0-7695-0149-4
DOI :
10.1109/CVPR.1999.786946