Abstract #2502
Dictionary Learning for Compressive T2 Mapping with Non-Cartesian Trajectories and Parallel Imaging
Benjamin Paul Berman 1 , Mahesh Bharath Keerthivasan 2 , Zhitao Li 2 , Diego R. Martin 3 , Maria I. Altbach 3 , and Ali Bilgin 2,4
1
Program in Applied Mathematics, University
of Arizona, Tucson, Arizona, United States,
2
Electrical
& Computer Engineering, University of Arizona, Tucson,
Arizona, United States,
3
Medical
Imaging, University of Arizona, Tucson, Arizona, United
States,
4
Biomedical
Engineering, University of Arizona, Tucson, Arizona,
United States
A non-Cartesian and multi-channel method of dictionary
learning and compressed sensing reconstruction leads to
improved T2 parameter mapping. The imaging problem is
constrained to have a sparse representation within a
dictionary. The principal components of the T2 decay are
reconstructed, and the addition of the dictionary
constraint leads to a reduction in noise and artifacts.
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