Abstract #4452
Optimizing Q-Space Sampling Density for Diffusion Spectrum Imaging
Qiyuan Tian 1 , Ariel Rokem 2 , Brian L. Edlow 3 , Rebecca D. Folkerth 4 , and Jennifer A. McNab 5
1
Department of Electrical Engineering,
Stanford University, Stanford, CA, United States,
2
Department
of Psychology, Stanford University, Stanford, CA, United
States,
3
Department
of Neurology, Massachusetts General Hospital, Boston,
MA, United States,
4
Department
of Pathology, Brigham and Women's Hospital, Boston, MA,
United States,
5
Department
of Radiology, Stanford University, Stanford, CA, United
States
Diffusion spectrum imaging is an approach to
characterizing complex tissue microstructure. Stronger
gradients enable expanded q-space coverage, which
improves the spin-displacement resolution but also
increases the q-space sampling density requirements.
Here, we show three datasets acquired on a whole, fixed,
human brain acquired with 300mT/m maximum gradients.
These data are used to examine the effects of q-space
sampling density on the fidelity of the voxel-wise
orientation distribution functions (ODFs). Specifically,
we show there is trade-off between ODF sharpness and
aliasing artifacts when sampling density is insufficient
to capture the spin-displacement pattern.
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