Faculty Bibliography

White matter fiber-tractography by means of diffusion tensor imaging (DTI) is a noninvasive technique that provides estimates of the structural connectivity of the brain. However, conventional fiber-tracking methods using DTI are based on echo-planar image acquisitions (EPI), which suffer from image distortions and artifacts due to magnetic susceptibility variations and eddy currents. Thus, a large percentage of white matter fiber bundles that are mapped using EPI-based DTI data are distorted, and/or terminated early, while others are completely undetected. This severely limits the potential of fiber-tracking techniques. In contrast, Turboprop imaging is a multiple-shot gradient and spin-echo (GRASE) technique that provides images with significantly fewer susceptibility and eddy current-related artifacts than EPI. The purpose of this work was to evaluate the performance of fiber-tractography techniques when using data obtained with Turboprop-DTI. All fiber pathways that were mapped were found to be in agreement with the anatomy. There were no visible distortions in any of the traced fiber bundles, even when these were located in the vicinity of significant magnetic field inhomogeneities. Additionally, the Turboprop-DTI data used in this research were acquired in less than 19 min of scan time. Thus, Turboprop appears to be a promising DTI data acquisition technique for tracing white matter fibers

The diffusion tensor (DT) is a three-dimensional (3D) model of diffusivity in biological tissues. In white matter (WM), the major eigenvector, which is the direction of greatest diffusivity, is generally assumed to align with the direction of the fiber bundles. The distribution of major eigenvectors in WM has been investigated using color-based maps and WM tractography (WMT). However, anatomical patterns in the medium and minor eigenvector directions have largely been ignored in DTI studies of the human brain. In this study, the patterns of medium and minor eigenvectors in the brain were investigated using both color-based maps and WMT. Specific WM structures, such as the corona radiata, internal and external capsules, sagittal stratum, cingulum, and superior longitudinal fasciculus, demonstrated coherent patterns in the medium and minor eigenvector directions. These patterns were consistent across subjects. The orthogonal or axial diffusion asymmetry may be explained by merging, diverging, or crossing fiber geometries. The effects of orthogonal diffusion asymmetry on WMT were also investigated. This study shows that WM axial asymmetry causes anisotropic dispersion patterns in the estimated tract trajectories. The medium and minor eigenvector patterns may be useful for elucidating the local dispersion distributions of WM tracts

White matter tractography is a noninvasive method for estimating and visualizing the white matter connectivity patterns in the human brain using diffusion tensor imaging (DTI) data. Sources of experimental noise may induce errors in the measured fiber directions, which will reduce the accuracy of the estimated white matter trajectories. In this study, a statistical nonparametric bootstrap method is described for estimating the dispersion and other errors in white matter tractography results. Prior studies have derived models of tractography error using the signal-to-noise ratio (SNR) and diffusion anisotropy of the DTI data. Tractography errors measured using bootstrap methods were generally consistent with an analytic model of tractography error except in areas where branching was evident. White matter tractography with bootstrap resampling is also applied to estimate the probabilities of connection between brain regions. The approach was used to generate probabilistic connectivity maps between the cerebral peduncles and specific cortical regions

White matter tractography using diffusion tensor MR images is a promising method for estimating the pathways of white matter tracts in the human brain. The success of this method ultimately depends upon the accuracy of the white matter tractography algorithms. In this study, a Monte Carlo simulation was used to investigate the impact of SNR, tensor anisotropy, and diffusion tensor encoding directions on the accuracy of six tractography algorithms. The accuracy was assessed in straight and curved tracts and tract geometries with divergence properties. In general, the tract dispersion increased with distance and decreased with SNR and anisotropy. The tract orientation with respect to the encoding scheme also influenced tract dispersion. Divergent tract geometries increased tract dispersion, whereas convergent tract geometries reduced dispersion. Analytic models of tract dispersion were constructed as a function of the tract distance, SNR, eigenvalues of the tracts, voxel size, and the relationship between the tract direction and the diffusion tensor encoding directions. In certain cases, the mean tract trajectory was found to deviate from the ideal pathway for curved trajectories. Analytical models of mean displacement were constructed as a function of the curvature, tract distance, step size, and tensor eigenvalues. These models may be used in future studies to assess the level of confidence associated with a tractography result

Diffusion tensor MRI provides unique directional diffusion information that can be used to estimate the patterns of white matter connectivity in the human brain. In this study, the behavior of an algorithm for white matter tractography is examined. The algorithm, called TEND, uses the entire diffusion tensor to deflect the estimated fiber trajectory. Simulations and imaging experiments on in vivo human brains were performed to investigate the behavior of the tractography algorithm. The simulations show that the deflection term is less sensitive than the major eigenvector to image noise. In the human brain imaging experiments, estimated tracts were generated in corpus callosum, corticospinal tract, internal capsule, corona radiata, superior longitudinal fasciculus, inferior longitudinal fasciculus, fronto-occipital fasciculus, and uncinate fasciculus. This approach is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions