A Stochastic Approach for Joint Learning of a Neural Network Reconstruction and Sampling Pattern in Cartesian 3D Parallel MRI

This work proposes a stochastic variation of the bias-accelerated subset selection (BASS) algorithm to learn an efficient sampling pattern (SP) for accelerated MRI. This algorithm is used in the joint learning of an SP and neural network reconstruction. We apply the proposed approach to two different 3D Cartesian parallel MRI problems. The proposed stochastic approach, when used for joint learning, improves the learning speed from 2.5X to 5X, obtaining SPs with similar properties as the non-stochastic approach with nearly the same RMSE and SSIM.