Abstract #3127
Accurate T2 Mapping with Sparisty and Linear Predictability Filtering
Xi Peng 1 , Leslie Ying 2 , Xin Liu 1 , and Dong Liang 1
1
Paul C. Lauterbur Research Centre for
Biomedical Imaging, Shenzhen Key Laboratory for MRI,
Shenzhen Institutes of Advanced Technology, Shenzhen,
Guangdong, China,
2
Department of Biomedical
Engineering, Department of Electrical Engineering, The
State University of New York at Buffalo, Buffalo, New
York, United States
Accelerating the acquisition of T2 mapping via sparse
sampling has drawn considerable attention. However, due
to non-ideal conditions in practical settings (i.e.,
insufficient sparsity/rank and coherent sampling),
errors occur in the T2-weighted images and the
subsequent relaxation map especially with high reduction
factors and noisy measurements. We address this issue by
integrating the prior information (i.e., exponential
functions) on the temporal signals into the image
reconstruction step. This is in contrast to the
conventional wisdom where the image reconstruction and
parameter mapping are performed independently. The
proposed method was demonstrated with an in-vivo brain
dataset and shows promising results.
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