Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence.

Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) both rely on spatial normalization to a template and yet have different requirements for the level of registration accuracy. VBM requires only global alignment of brain structures, with limited degrees of freedom in transformation, whereas TBM performs best when the registration is highly deformable and can achieve higher registration accuracy. In addition, the registration accuracy varies over the whole brain, with higher accuracy typically observed in subcortical areas and lower accuracy seen in cortical areas. Hence, even the determinant of Jacobian of registration maps is spatially varying in their accuracy, and combining these with VBM by direct multiplication introduces errors in VBM maps where the registration is inaccurate. We propose a unified approach to combining these 2 morphometry methods that is motivated by these differing requirements for registration and our interest in harnessing the advantages of both. Our novel method uses local estimates of registration confidence to determine how to weight the influence of VBM- and TBM-like approaches. Results are shown on healthy and mild Alzheimer\u27s subjects (N = 150) investigating age and group differences, and potential of differential diagnosis is shown on a set of Alzheimer\u27s disease (N = 34) and frontotemporal dementia (N = 30) patients compared against controls (N = 14). These show that the group differences detected by our proposed approach are more descriptive than those detected from VBM, Jacobian-modulated VBM, and TBM separately, hence leveraging the advantages of both approaches in a unified framework

Quantitative relaxometry and diffusion MRI for lateralization in MTS and non-MTS temporal lobe epilepsy.

We developed novel methodology for investigating the use of quantitative relaxometry (T1 and T2) and diffusion tensor imaging (DTI) for lateralization in temporal lobe epilepsy. Patients with mesial temporal sclerosis confirmed by pathology (N=8) and non-MTS unilateral temporal lobe epilepsy (N=6) were compared against healthy controls (N=19) using voxel-based analysis restricted to the anterior temporal lobes, and laterality indices for each MRI metric (T1, T2, fractional anisotropy (FA), mean diffusivity, axial and radial diffusivities) were computed based on the proportion of significant voxels on each side. The diffusivity metrics were the most lateralizing MRI metrics in MTS and non-MTS subsets, with significant differences also seen with FA, T1 and T2. Patient-specific multi-modal laterality indices were also computed and were shown to clearly separate the left-onset and right-onset patients. Marked differences between left-onset and right-onset patients were also observed, with left-onset patients exhibiting stronger laterality indices. Finally, neocortical abnormalities were found to be more common in the non-MTS patients. These preliminary results on a small sample size support the further investigation of quantitative MRI and multi-modal image analysis in clinical determination of seizure onset. The presence of more neocortical abnormalities in the non-MTS group suggests a role in seizure onset or propagation and motivates the investigation of more sensitive histopathological analysis to detect and delineate potentially subtle neocortical pathology

Anatomical Image Series Analysis in the Computational Anatomy Random Orbit Model

Serially acquired medical imagery plays an important role in the computational study of human anatomy. In this work, we describe the development of novel algorithms set in the large deformation diffeomorphic metric mapping framework for analyzing serially acquired imagery of two general types: spatial image series and temporal image series. In the former case, a critical step in the analysis of neural connectivity from serially-sectioned brain histology data is the reconstruction of spatially distorted image volumes and registration into a common coordinate space. In the latter case, computational methods are required for building low dimensional representations of the infinite dimensional shape space standard to computational anatomy. Here, we review the vast body of work related to volume reconstruction and atlas-mapping of serially-sectioned data as well as diffeomorphic methods for longitudinal data and we position our work relative to these in the context of the computational anatomy random orbit model. We show how these two problems are embedded as extensions to the classic random orbit model and use it to both enforce diffeomorphic conditions and analyze the distance metric associated to diffeomorphisms. We apply our new algorithms to histology and MRI datasets to study the structure, connectivity, and pathological degeneration of the brain