Accelerating MR parameter mapping using nonlinear manifold learning and supervised pre-imaging
In this paper, we propose a new reconstruction framework that utilizes nonlinear models to sparsely represent the MR parameter-weighted image in a high dimensional feature space. Different from the prior work with nonlinear models where the image series is reconstructed simultaneously, each image at a specific time point is assumed to lie in a low-dimensional manifold and is reconstructed individually. The low-dimensional manifold is learned from the training images generated by the parametric model. To reconstruct each image, among infinite number of solutions that satisfy the data consistent constraint, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick and split Bregman iteration algorithm. The proposed method was evaluated on a set of in-vivo brain T2 mapping data set and shown to be superior to the conventional compressed sensing methods.