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

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

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.