k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-Temporal Correlations
Dynamic magnetic resonance imaging (MRI) exhibits high correlations in k-space and time. In order to accelerate the dynamic MR imaging and to exploit k-t correlations from highly undersampled data, here we propose a novel deep learning based approach for dynamic MR image reconstruction, termed k-t NEXT (k-t NEtwork with X-f Transform). In particular, inspired by traditional methods such as k-t BLAST and k-t FOCUSS, we propose to reconstruct the true signals from aliased signals in x-f domain to exploit the spatio-temporal redundancies. Building on that, the proposed method then learns to recover the signals by alternating the reconstruction process between the x-f space and image space in an iterative fashion. This enables the network to effectively capture useful information and jointly exploit spatio-temporal correlations from both complementary domains. Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
This work was supported by EPSRC programme grant SmartHeart (EP/P001009/1).
- 5.Han, Y., Ye, J.C.: k-space deep learning for accelerated MRI. arXiv preprint arXiv:1805.03779 (2018)
- 11.Liu, J., Kuang, T., Zhang, X.: Image reconstruction by splitting deep learning regularization from iterative inversion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 224–231. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_26CrossRefGoogle Scholar
- 17.Zhang, P., Wang, F., Xu, W., et al.: Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in k-space. In: MICCAI, pp. 180–188 (2018)Google Scholar
- 18.Zhang, S., Block, K.T., Frahm, J.: Magnetic resonance imaging in real time: advances using radial FLASH. Magn. Reson. Med. 31(1), 101–109 (2010)Google Scholar