Abstract #3808
Accelerating Dynamic MRI via Tensor Subspace Learning
Morteza Mardani 1 , Leslie Ying 2 , and Georgios B Giannakis 3
1
University of Minnesota, Falcon Heights, MN,
United States,
2
Buffalo
University, New York, United States,
3
University
of Minnesota, Minneapolis, MN, United States
Our advocated approach builds on three-way tensors and
leverages spatiotemporal correlations of the ground
truth images through tensor low rank. CP/PARAFAC
decomposition of tensors is adapted [7], and a
tomographic approach is put forth that leverages the
tensor low rank to recursively learn the low-dimensional
subspace from undersampled k-space data. In the
nutshell, the novel approach allows real-time data
acquisition without gating or breath-holding, yet being
able to recover high-quality dynamic cardiac images from
high-dimensional even under-sampled tensors
`on-the-fly'. It means the images can be reconstructed
while the data is still being acquired.
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