Real time dynamic MRI by exploiting spatial and temporal sparsity.

Online imaging requires that the reconstruction of current frame only depends on the previous frames, and real time imaging is the desired case. In this work, we propose a novel scheme for real time dynamic magnetic resonance imaging (dMRI) reconstruction. Different from previous methods, the reconstructions of the second frame to the last frame are independent in our scheme, which only require the first frame as the reference image. Therefore, this scheme can be naturally implemented in parallel. After the first frame is reconstructed, all the later frames can be processed as soon as the k-space data are acquired. As an extension of the conventional spatial total variation, a new online model called dynamic total variation is used to exploit the sparsity on both spatial and temporal domains in dMRI. In real time dMRI, each frame is required to be reconstructed very fast. We then design a novel reweighted least squares algorithm to solve the challenging problem. Motivated by the special structure of partial Fourier transform in sparse MRI, this algorithm is accelerated by the preconditioned conjugate gradient descent method. The proposed method is compared with 4 state-of-the-art online and offline methods on two in-vivo cardiac dMRI datasets. The experimental results show that our method significantly outperforms previous online methods, and is comparable to the offline methods in terms of reconstruction accuracy.