Yohan Jun1, Hyungseob Shin1, Taejoon Eo1, and Dosik Hwang1
1Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
1Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
We propose a Joint
Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet),
which jointly reconstructs an MR image and coil sensitivity maps from
undersampled multi-coil k-space data using deep learning networks combined with
MR physical models.
Fig. 1. Overall framework of our proposed joint deep model-based
magnetic resonance image and coil sensitivity reconstruction network
(Joint-ICNet).
Fig. 3. Fully sampled and reconstructed (a) FLAIR images and (b) T2 images using
zero-filling, ESPIRiT, U-net, k-space learning, DeepCascade, and Joint-ICNet,
with the reduction factors R = 4 and R =8, respectively.
