In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback-Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.

No abstract

Abstract. The construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intrapopulation variability and inter-population differences, to provide voxelwise mapping of functional sites, and to facilitate tissue and object segmentation via registration of anatomical labels. We formulate the unbiased atlas construction problem as a Fréchet mean estimation in the space of diffeomorphisms via large deformations metric mapping. A novel method for computing constant speed velocity fields and an analysis of atlas stability and robustness using entropy are presented. We address the question: how many images are required to build a stable brain atlas?

Fluid attenuated inversion recovery (FLAIR) and diffusion tensor imaging (DTI) techniques have been widely used to evaluate white matter (WM) alterations associated with aging, dementia and cerebral vascular disease. The relationship between FLAIR detected WM lesions (WML) and DTI detected WM integrity changes, however, remains unclear. To investigate this association, voxelwise correlations between 4 Tesla DTI and FLAIR images from elderly subjects were performed by relating WML volume and intensity in FLAIR to fractional anisotropy (FA) and mean diffusivity (MD) in DTI. Significant DTI-FLAIR correlations were found in regions overlapping with the WML of moderate intensities in FLAIR. No significant correlations were detected in periventricular regions where the FLAIR intensities are particularly high. The findings are consistent with a transitional model for WM degeneration from normal WM to cerebrospinal fluid (CSF). The results show that the correlation between DTI and FLAIR disappears when the FLAIR intensity of WML reaches its maximum at a certain lesion severity, and that the correlations may remerge with reversed signs when the lesion severity is further increased. These results suggest that the different stages of WM degeneration in elderly subjects can be better characterized by regional DTI-FLAIR correlations than single modality alone.

No abstract