Title: Approximating anatomical brain connectivity with diffusion tensor mri via anisotropic diffusion simulations
Abstract: Diffusion tensor magnetic resonance imaging (DT-MRI) is a new MRI modality based on exploiting water diffusivity. During the last decade, it has raised promises for achieving a better comprehension of the white matter anatomy and reconstructing the human brain anatomical connectivity, which plays an indispensable role in neurosurgery planning and in tackling brain diseases and disorders. A variety of techniques for reconstructing white matter tracts and estimating brain connectivity have been developed since the appearance of DT-MRI. However, there are two crucial issues that have great impacts on the performance of DT-MRI based tractography algorithms. One is that image data noise cannot be avoided during the acquisition of diffusion-weighted images, while the other involves the effects of partial voluming, which arises when more than one fiber population exists within a single voxel.
The main purpose of this work is to develop a class of new tractography techniques with enhanced robustness and reliability in reconstructing white matter fiber pathways on the DT-MRI platform. The mechanism of the proposed fiber tracking algorithms relies on simulating the diffusion process in the brain, which is conducted on a computational framework that we have implemented for solving the time-dependent anisotropic diffusion equation, tailored to the cerebral circumstance. Synthetic and real diffusion tensor data are employed to demonstrate the performance of the tracking schemes. Experimental results show that the synthetic tracts are accurately replicated, and major white matter fiber pathways can be reproduced noninvasively, with the tract branching and crossing being accommodated. Since simulating the diffusion process makes full use of the diffusion tensor, including both the magnitude and the orientation information, the proposed approaches bear great potential to palliate the impacts of fiber crossing and diverging as well as to improve the immunity to noise.
We also conduct performance evaluation between a streamline-type tracking algorithm and diffusion simulation based tractography, using noise-perturbed synthetic diffusion tensor fields with different fiber geometries. Another important part we include in this work is the application of the diffusion simulation based tractography, which is exploited to explore the integrity of white matter fibers in patients with amnestic mild cognitive impairment.
Keywords. Anatomical brain connectivity, fiber tractography, anisotropic diffusion simulation, diffusion tensor MRI, mild cognitive impairment.
Publication Year: 2006
Publication Date: 2006-01-01
Language: en
Type: article
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