A noise robust image reconstruction deep neural network with cycle interpolation

We propose a new Parallel Imaging scheme using a deep neural network which performs well with fewer ACS line in noisy environments. The proposed scheme includes ACS loss which is used in RAKI and cycle interpolation loss that we newly propose in our work. RAKI generalized GRAPPA in noisy environments by applying non-linear k-space interpolation with a deep neural network. However, it requires additional ACS lines to output satisfactory performance. Here, we suggest a new scheme to overcome the reconstruction performance in a noisy environment with fewer ACS lines.