Real Time Dynamic MRI with Dynamic Total Variation

In this study, 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. 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 is acquired. As an extension of the convention total variation, a new online model called dynamic total variation is used to exploit the sparsity on both spatial and temporal domains. In addition, we design an accelerated reweighted least squares algorithm to solve the challenging reconstruction problem. This algorithm is motivated by the special structure of partial Fourier transform in sparse MRI. The proposed method is compared with 4 state-of-the-art online and offline methods on in-vivo cardiac dMRI datasets. The results show that our method significantly outperforms previous online methods, and is comparable to the offline methods in terms of reconstruction accuracy.