Tools for multiple granularity analysis of brain MRI data for individualized image analysis.

Voxel-based analysis is widely used for quantitative analysis of brain MRI. While this type of analysis provides the highest granularity level of spatial information (i.e., each voxel), the sheer number of voxels and noisy information from each voxel often lead to low sensitivity for detection of abnormalities. To ameliorate this issue, granularity reduction is commonly performed by applying isotropic spatial filtering. This study proposes a systematic reduction of the spatial information using ontology-based hierarchical structural relationships. The 254 brain structures were first defined in multiple (n=29) geriatric atlases. The multiple atlases were then applied to T1-weighted MR images of each subject's data for automated brain parcellation and five levels of ontological relationships were established, which further reduced the spatial dimension to as few as 11 structures. At each ontology level, the amount of atrophy was evaluated, providing a unique view of low-granularity analysis. This reduction of spatial information allowed us to investigate the anatomical features of each patient, demonstrated in an Alzheimer's disease group.

Diffeomorphic brain mapping based on T1-weighted images: improvement of registration accuracy by multichannel mapping.

PURPOSE: To improve image registration accuracy in neurodegenerative populations. MATERIALS AND METHODS: This study used primary progressive aphasia, aged control, and young control T1-weighted images. Mapping to a template image was performed using single-channel Large Deformation Diffeomorphic Metric Mapping (LDDMM), a dual-channel method with ventricular anatomy in the second channel, and a dual-channel with appendage method, which utilized a priori knowledge of template ventricular anatomy in the deformable atlas. RESULTS: Our results indicated substantial improvement in the registration accuracy over single-contrast-based brain mapping, mainly in the lateral ventricles and regions surrounding them. Dual-channel mapping significantly (P < 0.001) reduced the number of misclassified lateral ventricle voxels (based on a manually defined reference) over single-channel mapping. The dual-channel (w/appendage) method further reduced (P < 0.001) misclassification over the dual-channel method, indicating that the appendage provides more accurate anatomical correspondence for deformation. CONCLUSION: Brain anatomical mapping by shape normalization is widely used for quantitative anatomical analysis. However, in many geriatric and neurodegenerative disorders, severe tissue atrophy poses a unique challenge for accurate mapping of voxels, especially around the lateral ventricles. In this study we demonstrate our ability to improve mapping accuracy by incorporating ventricular anatomy in LDDMM and by utilizing a priori knowledge of ventricular anatomy in the deformable atlas.