Abstract #0984
Removal of Nuisance Signals from Limited and Sparse 3D 1 H-MRSI Data of the Brain
Bryan Clifford 1 , Chao Ma 2 , Fan Lam 1 , and Zhi-Pei Liang 1
1
Electrical and Computer Engineering,
University of Illinois at Urbana-Champaign, Urbana, IL,
United States,
2
Beckman
Institute, University of Illinois at Urbana-Champaign,
Urbana, IL, United States
We present a post-processing method for the removal of
water and lipid signals from 3D
1
H-MRSI
data that has limited and sparse coverage of
(k,
t)
-space. Our method extends a recently proposed
Union-of-Subspace method to enable the use of support
constraints derived from high-resolution 3D anatomical
scans. The method is capable of handling 3D data sets
with only a limited number of spatial encodes in the
slice direction. Experimental results show that the
proposed method can effectively remove water and lipid
signals from 3D
1
H-MRSI
data of the brain. The method is particularly useful for
accelerated
1
H-MRSI with sparse sampling.
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