[Show abstract][Hide abstract] ABSTRACT: To develop and apply diffusion tensor imaging (DTI)-based normalization methodology for the detection and quantification of sites of traumatic brain injury (TBI) and the impact of injury along specific brain pathways in (a) individual TBI subjects and (b) a TBI group.
Normalized DTI tractography was conducted in the native space of 12 TBI and 10 age-matched control subjects using the same number of seeds in each subject, distributed at anatomically equivalent locations. Whole-brain tracts from the control group were mapped onto the head of each TBI subject. Differences in the fractional anisotropy (FA) maps between each TBI subject and the control group were computed in a common space using a t test, transformed back to the individual TBI subject's head space, and thresholded to form regions of interest (ROIs) that were used to sort tracts from the control group and the individual TBI subject. Tract counts for a given ROI in each TBI subject were compared to group mean for the same ROI to quantify the impact of injury along affected pathways. The same procedure was used to compare the TBI group to the control group in a common space.
Sites of injury within individual TBI subjects and affected pathways included hippocampal/fornix, inferior fronto-occipital, inferior longitudinal fasciculus, corpus callosum (genu and splenium), cortico-spinal tracts and the uncinate fasciculus. Most of these regions were also detected in the group study.
The DTI normalization methodology presented here enables automatic delineation of ROIs within the heads of individual subjects (or in a group). These ROIs not only localize and quantify the extent of injury, but also quantify the impact of injury on affected pathways in an individual or in a group of TBI subjects.
Magnetic Resonance Imaging 08/2009; 28(1):22-40. · 2.06 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRI-postmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects.
International Journal of Imaging Systems and Technology 02/2008; 18(5-6):325-335. · 0.64 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Partial volume effects are one of the most common sources of error in diffusion tensor imaging (DTI) tractography. For example, in data from older subjects or Alzheimer's disease probable subjects, the situation is especially exacerbated around the dilated ventricle, which causes erroneous merging of tracts. Rescanning the subject at higher resolution is the best solution, but often times unattainable. We offer a retrospective filtering algorithm, which is purely subtractive, based on a region of interest (ROI) filtering methodology that filters tracts by their shape and seed points. The ROIs are defined using both anatomic images and fractional anisotropy (FA) maps in normalized space allowing for consistency across all subjects. Our algorithm helps correct the partial volume effects by reducing the overestimation of tract length, giving a more accurate regional tract count. The objective of our retrospective algorithm is reclamation of data sets from partial volume effects.
[Show abstract][Hide abstract] ABSTRACT: Small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples. Using non-linear polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic examinations of histological samples drawn off the postmortem brain. Digital photographs of postmortem slices served as an intermediate reference to coregister the MRIs to microscopy. In-vivo MRI to postmortem coregistration is challenging due to gross structural deformations in the brain during extraction. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volume in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information produced the best MRI-postmortem coregistration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of the microscopically identified vascular lesions and their MRI-postmortem coregistered locations. The results show a mean 3D displacement of 7.5 plusmn 2.7 mm between these locations for 11 vascular lesions in 7 subjects.
[Show abstract][Hide abstract] ABSTRACT: In the absence of ground truth, there are very few methods available to evaluate the accuracy of a specific tracking algorithm or the various data acquisition protocols for DTI-tractography. The objective of this work was to develop methodology, based on tract-length histograms, that could be used to evaluate whole-brain tractography with data acquired under different conditions for a given subject, for example six versus 25 gradient directions, or use of an 8-element phased array versus quadrature head-coil. Whole-brain DTI data were acquired from six healthy normal human volunteers on a 1.5 T GE scanner at TR=10.3s, field-of-view 26cm, 128x128 matrix, 28 contiguous 4mm thick slices from 25 isotropic gradient directions with b=1000s/mm2, one b=0 acquisition, and number of excitations (NEX)=1 for a total acquisition time of 3min 53s. Similarly, four sets of data were acquired from 6 non-colinear directions and combined with two b=0 acquisitions to equalize the time for 25 and 6-directions acquisitions. The tract-length histograms clearly show that at equal acquisition time, there are more long tracts in the 25-direction acquisition than the 6-direction acquisition, suggesting better estimation of the tensor with 25 directions. Tract-counts above a threshold provide an objective index to evaluate tractography. Also a comparison of the two coils shows a higher tract-count for long tracts with the 8-element coil, consistent with the demonstrated higher sensitivity and higher signal-to-noise ratio for EPI acquisitions by the 8-element coil.
[Show abstract][Hide abstract] ABSTRACT: Introduction Though there are numerous publications on voxel based whole-brain FA group comparisons, there are very few reports of similar comparisons using whole-brain tractography. Tractography comparisons are usually based on sorting ROI-constrained tracts in subject space, which are then voxelated and mapped to a normalized space for group studies. This requires a priori hypotheses to identify tracts and may miss key differences. Another approach is based on averaging tensors in normalized space  but does not fully account for inherent primary eigenvector variations in individuals. Voxel based normalization procedures which incorporate tensor correction  generally impart smoothing and frequently divide known tracts into non-contiguous segments. A recent method of tract segmentation by choosing anatomically equivalent seed points in subject space in conjunction with similarity measures  would work under certain conditions but is not applicable to general whole-brain tractography where no particular tracts are specified. We have developed a method to perform whole-brain tractography group comparisons where the same number of seed points is used in each subject at anatomically equivalent positions throughout the entire brain. All tracts contained within each voxel per subject are mapped into a normalized space while retaining each tract's continuity. Tractography for each subject is then voxelated in normalized space using a "weighted count" (WC) of tracts per voxel to impart a quantitative measure of connectivity to voxel intensities, thereby providing a framework for group comparisons. As all tracts reside in normalized space, it becomes possible to show directly tracts/pathways affected by regions that are delineated in group comparisons. Details of the approach and its application to detect pathways affected in Alzheimer Disease (AD) are reported here. Also whole-brain tractography differences are compared to whole-brain FA differences to illustrate consistency and complementarities of the two approaches. Method FLAIR diffusion weighted images (DWI) were acquired in 5 mm thick slices on a 1.5 T Siemens scanner from 10 age-matched Normal Controls (NC) and 15 probable AD subjects using six encoding gradient directions at b-values of 0, 160, 360, 640, and 1000 sec/mm 2 . A customized FA template was created from the 10 NC subjects through a two-step normalization procedure where segmented white matter voxels, co-registered to the b 0 images, were first normalized to the MNI white-matter template using a 12 parameter affine/non-linear transformation. This minimized ventricular variations among subjects. The resulting parameters were refined by a whole-brain EPI to EPI normalization, and applied to individual FA maps to create the FA template. This FA template was used to map the center points of all voxels (seed points) from normalized space to each subject's space (point to point mapping), creating the same number and anatomically equivalent distribution of seed-points in each subject. All tracts from each subject (streamline tractography, 0.2mm step size, FA<0.15, deflection<45°) were similarly mapped back to normalized space on a point-to-point basis from all seeds contained within a common mask for the entire population (AD + NC). When interpolation is used, all voxels connected to a seed voxel generally send tracts back into the same seed voxel. Thus the number of tracts intersecting a voxel is a reflection of how many voxels are connected to it, which provides a convenient metric to quantify "connectivity". We refine this measure by weighting the count of tracts per voxel by the length of the intercept of each tract, and call this the "weighted count" metric, which then becomes the voxel intensity for group comparisons.