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Abstract #4375

Image Reconstruction from Highly Undersampled (K, T)-Space Data with Joint Partial Separability &

Bo Zhao1, Justin Haldar1, Anthony Christodoulou1, Zhi-Pei Liang1

1Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Sparse sampling is emerging as an effective tool to further accelerate MRI. Previous work has shown that partial separability and sparsity constraints are each able to individually reduce sampling requirement below the Nyquist rate. In this abstract, we present a new reconstruction method that enables using partial separability and sparsity constraints jointly. The joint use of these constraints enables high resolution reconstruction from sparsely sampled data.