Representative Brain Models for 3-D Brain Imaging
Thursday, October 12, 2000 - 4:00pm - 4:45pm
Jack Lancaster (University of Texas Health Science Center)
Our long-term goal is to develop 3-D brain models that retain consistent anatomical features of the group of brains from which they are derived. Such brain models could serve as representative brains for comparisons between groups of interest, i.e. testing for anatomical differences between normal and disease groups. An analysis method based on 3-D deformation fields is proposed for developing representative brain models. The hypothesis is that a target brain (our brain model) that represents the least deformation effort for a group is the best overall representative brain for the group. The process can be thought of as a two step procedure, where the first step is to find the best target brain within the group of brain images, followed by an optimization process that transforms this best target brain into an optimal brain model for the group. A target quality score to measure deformation effort and anatomical variability was devised to guide this processing. The complexity of the processing scheme is O(n^2) where n is the number of brains in the group and number of deformation fields to calculate. Even with high-speed regional warping methods such as octree spatial normalization this would be a lengthy process for large groups of brains. A fast algorithm (O(n)) was found to provide similar results. A review of the processing steps for the development of representative brain models will be presented.