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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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