Fast MRSI Reconstruction Combining Linear and Nonlinear Manifold Models

A computationally efficient MRSI reconstruction method is presented. The proposed problem formulation integrates a subspace model of the high-dimensional spatiotemporal function (SPICE) and a network-based learned projector on to a low-dimensional manifold of generic spectroscopic signals. The subspace representation allows for more flexible spatiotemporal sampling designs than using nonlinear manifold constraint alone, while the manifold constraint effectively regularizes the subspace fitting, especially at higher orders. An efficient algorithm is designed to solve the optimization problem. The benefits of the proposed synergy have been demonstrated using simulations as well as experimental 31P and 1H-MRSI data.