Registration of brain structures should bring anatomically equivalent areas into correspondence which is usually done using information from structural MRI modalities. Correspondence can be improved by using other image modalities that provide complementary data. In this paper we propose and evaluate two novel surface registration algorithms which improve within-surface correspondence in brain structures. Both approaches use a white-matter tract similarity function (derived from probabilistic tractography) to match areas of similar connectivity patterns. The two methods differ in the way the deformation field is calculated and in how the multi-scale registration framework is implemented. We validated both algorithms using artificial and real image examples, in both cases showing high registration consistency and the ability to find differences in thalamic sub-structures between Alzheimer's disease and control subjects. The results suggest differences in thalamic connectivity predominantly in the medial dorsal parts of the left thalamus.

Type

Conference paper

Publication Date

2009

Volume

12

Pages

705 - 712

Keywords

Algorithms, Artificial Intelligence, Diffusion Tensor Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Subtraction Technique, Thalamus