Unifying the analyses of anatomical and diffusion tensor images using volume-preserved warping.

The general rubric of magnetic resonance imaging (MRI) subsumes various modalities of data acquisition (e.g., T1- and T2-weighted anatomical imaging, magnetic resonance spectroscopy, and diffusion tensor (DT) imaging (DTI)), each of which provides unique but complementary information about brain structure and function. Combining data from multiple MRI modalities can provide a more comprehensive view of a subject or group of subjects than can any single MRI modality. For example, anatomical T1- or T2-weighted MR images can help to identify structural boundaries within the gray matter and white matter of the human cerebrum, whereas DTI and its derived measures (e.g., fractional anisotropy [FA], apparent diffusion coefficient [ADC]) provide information about the directional organization of brain tissue that can be used to track nerve-fiber pathways. Moreover, a map of the principal directions for the diffusion of water, generated using DTI, can help to more accurately parcellate the anatomy of the corpus callosum (CC) and other white matter structures (e.g., cingulum, external capsule, and anterior thalamic radiation) (1-4) that appear homogeneous in their contrast and signal intensities in T1- or T2-weighted images. Researchers have long desired to integrate information from both modalities to provide a more comprehensive view of anatomical structure and connectivity in the human brain. The integration of T1-weighted and DT imaging data, however, faces at least two major obstacles: 1) the accurate coregistration of datasets from the two modalities, which differ profoundly in their contrast and possibly in the geometric distortion that they contain, and 2) the accurate selection of anatomically relevant regions of interest (ROIs) within the coregistered dataset that can serve as seed points for the identification and tracking of a relevant subset of never fibers within the brain. We will discuss each of these obstacles in turn in more detail. The accurate coregistration of T1-weighted anatomical and DT images usually requires the implementation of nonlinear warping techniques, which is challenging for several reasons. First, because DT images are acquired using echo-planar imaging (EPI) pulse sequences, they are more prone than are anatomical T1-or T2-weighted images (which are usually not acquired using EPI) to spatial distortions arising from susceptibility artifacts at the interface of different media (e.g., at the interface of brain tissue and air in the sinuses), to eddy-current artifacts caused by the requisite switching of imaging gradients during the acquisition of DTI datasets, and to other distortions caused by inhomogeneities in the static magnetic field. Coregistering accurately images that contain differing kinds and degrees of geometric distortion is both difficult and complex. Second, image resolution and signal-to-noise ratios (SNRs) are higher in T1- or T2-weighted datasets than in DTI datasets, and these differences may introduce errors in the identification of corresponding structures across the images during their coregistration. Avoiding these errors requires additional imaging processing to make the resolution and SNR of the two types of images more comparable. Third, voxels in the two types of images contain data of differing dimensionality. Anatomical T1- or T2-weighted images contain one-dimensional, scalar data (i.e., only one intensity value per voxel), whereas DT images are multidimensional data-sets (i.e., they contain 3 × 3 symmetric, positive definite matrices at each voxel that represent the probability of the spatial diffusion of water molecules). Finally, these multidimensional DTI datasets represent tensors, which encode information about the spatial orientation of nerve fibers; thus any warping of DT images, either during coregistration with anatomical images or during spatial normalization of DTI datasets across individuals, requires careful preservation of the orientation and shape of tensors in order to maintain the integrity of that biologically relevant information. Added to the difficulty of coregistration is the problem of identifying and reliably segmenting ROIs that are morphometrically valid and anatomically relevant to the neural systems under investigation. This process is difficult because no two brains are exactly alike, and within many brain regions, including within the cerebral cortex, the contrast of both anatomical boundaries and internal structure can be insufficient for the reliable and valid delineation of anatomically discrete structures. Automated segmentation has been an elusive goal in the processing of medical images, leaving expert anatomical knowledge as the sole basis for the manual segmentation or definition of brain ROIs. The manual definition of ROIs, however, is problematic because different experts usually produce different segmentations of the same image (5), and even the same expert will produce differing segmentations when segmenting the same image twice (6). The reliability and validity of segmentation can suffer even when subdividing highly discrete anatomical structures, such as the CC. Moreover, the manual definition of ROIs is usually time-consuming and financially costly. Numerous attempts have been made to integrate information from T1- or T2-weighted images and DTI datasets (Refs. 7-12 are but a few examples), and several of these have also sought to address either the difficulty of cross-modal registration or the difficulty of accurate identification of ROIs used for the initiation of fiber tracking. To our knowledge, however, no method for multimodal imaging has yet been developed that simultaneously addresses the challenges of both coregistration and ROI delineation. Some groups, for example, have proposed elegant deformation algorithms that improve the quality of cross-modal coregistration (13-21), and others have used existing software packages for cross-modal coregistration, such as statistical parametric mapping (SPM) (18,19) and automated image registration (AIR) (20,21). These methods, however, have not been integrated with methods for the automated delineation of ROIs as a basis for fiber tracking. Other groups, in contrast, have attempted to reduce inaccuracies in the delineation of ROIs by generating an atlas of the brain through the averaging of manual segmentations from a group of experts (6), and then using that atlas as a template for automated ROI delineation in additional subjects. These methods for reducing inaccuracies in ROI delineation, however, do not address the problem of coregistering images across anatomical and DTI datasets. Thus, previously proposed methods for integrating information from T1-weighted and DTI datasets leave unsolved one or the other of the major difficulties in unifying the analyses of anatomical and DTI datasets. To more comprehensively address these challenges in integrating the analysis of anatomical images and DTI datasets, we have developed an automated framework that identifies localized group differences in brain structure in spatially normalized, T1-weighted images, and that then uses those localized differences as seeding ROIs for the tracking of fiber pathways in the brain within DT images. Our framework employs volume-preserved warping (VPW) to automatically identify the morphometric differences between groups of subjects. We tested the performance of our framework on multimodal MRI data from a group of children with Tourette’s syndrome (TS) and a group of age-matched normal controls (NC). After tracking fibers in the two diagnostic groups, we clustered the fibers tracts into bundles and then identified bundles with similar and differing 3D morphologies across the two groups of subjects.