Abstract #4488
Optimization of a Fast Diffusion Estimation Two-Compartment Model for Diffusion Tensor Imaging
Andrew R. Hoy 1,2 , Chen Guan Koay 1 , Steven R. Kecskemeti 2,3 , and Andrew L. Alexander 1,2
1
Medical Physics, University of Wisconsin,
Madison, Wisconsin, United States,
2
Waisman
Laboratory for Brain Imaging and Behavior, Madison,
Wisconsin, United States,
3
Radiology,
University of Wisconsin, Madison, Wisconsin, United
States
Diffusion tensor imaging yields information about tissue
microstructure. However, when a single voxel contains
tissue and free water, DTI is not appropriate. A
two-tensor fast diffusion estimation model has been
proposed to correct this shortcoming. This model was
implemented in a novel manner, and the acquisition
parameters optimized through Monte Carlo simulations.
The optimal acquisition with 68 diffusion-weighted
encoded images had three diffusion-weighted shells
(b-value in s/mm2 x number of directions) of 200x12,
650x40, 1500x12. This was confirmed in vivo. The model
is useful for tissues adjacent to CSF and removing
artifacts from CSF blurring and ghosting.
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