Person:

Rathi, Yogesh

We describe a technique that uses tractography to drive the local fiber model estimation. Existing techniques use independent estimation at each voxel so there is no running knowledge of confidence in the estimated model fit. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by those previous. To do this we perform tractography within a filter framework and use a discrete mixture of Gaussian tensors to model the signal. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model to the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Using two- and three-fiber models we demonstrate in synthetic experiments that this approach significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.

Background—Previous studies have shown that frontostriatal networks, especially those involving dorsolateral prefrontal cortex (DLPFC) and ventrolateral prefrontal cortex (VLPFC) mediate cognitive functions some of which are abnormal in schizophrenia. This study examines white matter integrity of the tracts connecting DLPFC/VLPFC and striatum in patients with firstepisode schizophrenia (FESZ), and their associations with cognitive and clinical correlates. Methods—Diffusion tensor and structural magnetic resonance images were acquired on a 3T GE Echospeed system from 16 FESZ and 18 demographically comparable healthy controls. FreeSurfer software was used to parcellate regions of interest. Two-tensor tractography was applied to extract fibers connecting striatum with rostral middle frontal gyrus (rMFG) and inferior frontal gyrus (IFG), representing DLPFC and VLPFC respectively. DTI indices, including fractional anisotropy (FA), trace, axial diffusivity (AD) and radial diffusivity (RD), were used for group comparisons. Additionally, correlations were evaluated between these diffusion indices and the Wisconsin Card Sorting Task (WCST) and the Brief Psychiatric Rating Scale (BPRS). Results—FA was significantly reduced in the left IFG-striatum tract, whereas trace and RD were significantly increased in rMFG-striatum and IFG-striatum tracts, bilaterally. The number of WCST categories completed correlated positively with FA of the right rMFG-striatum tract, and negatively with trace and RD of right rMFG-striatum and right IFG-striatum tracts in FESZ. The BPRS scores did not correlate with these indices. Conclusions—These data suggest that white matter tract abnormalities between rMFG/IFG and striatum are present in FESZ and appear to be significantly associated with executive dysfunction but not with symptom severity.

We propose a technique to simultaneously estimate the local fiber orientations and perform multifiber tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the measured signal or estimated fiber orientation. Further, to overcome noise, many algorithms use a filter as a post-processing step to obtain a smooth trajectory. We formulate fiber tracking as causal estimation: at each step of tracing the fiber, the current estimate of the signal is guided by the previous. To do this, we model the signal as a discrete mixture of Watson directional functions and perform tractography within a filtering framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides an accurate estimate of the local structure at each point along the fiber. We choose the Watson function since it provides a compact representation of the signal parameterized by the principal diffusion direction and a scaling parameter describing anisotropy, and also allows analytic reconstruction of the oriented diffusion function from those parameters. Using a mixture of two and three components (corresponding to two-fiber and three-fiber models) we demonstrate in synthetic experiments that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments examine the corpus callosum and internal capsule and confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.

Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

This paper proposes a novel framework for joint orientation distribution function (ODF) estimation and tractography based on a new class of tensor-kernels. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the measured signal or estimated fiber orientation. In this work, fiber tracking is formulated as recursive estimation: at each step of tracing the fiber, the current estimate of the ODF is guided by the previous. To do this, second and higher order tensor based kernels are employed. A weighted mixture of these tensor-kernels is used for representing crossing and branching fiber structures. While tracing a fiber, the parameters of the mixture model are estimated based on the ODF at that location and a smoothness term that penalizes deviation from the previous estimate along the fiber direction. This ensures smooth estimation along the direction of propagation of the fiber. In synthetic experiments, using a mixture of two and three components it is shown that this approach improves the angular resolution at crossings. In vivo experiments using two and three components examine the corpus callosum and corticospinal tract and confirm the ability to trace through regions known to contain such crossing and branching.