k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
This work addresses faster and more accurate dynamic MRI reconstruction for medical imaging applications, representing an incremental advance over existing methods.
The paper tackled dynamic MRI reconstruction from highly undersampled data by proposing k-t NEXT, a deep learning method that exploits spatio-temporal correlations in x-f and image domains, achieving state-of-the-art performance on cardiac cine MRI scans with quantitative and qualitative improvements.
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.