Jaejin Cho1,2, Qiyuan Tian1,2, Robert Frost1,2, Itthi Chatnuntawech3, and Berkin Bilgic1,2,4
1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Havard Medical School, Cambridge, MA, United States, 3National Nanotechnology Center, Pathum Thani, Thailand, 4Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Havard Medical School, Cambridge, MA, United States, 3National Nanotechnology Center, Pathum Thani, Thailand, 4Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
Simultaneously training wave-encoded model-based deep learning (wave-MoDL) with hybrid k- and image-space priors and a U-net enables high-fidelity image reconstruction and segmentation performance at high acceleration rates.
Figure 1. a. the proposed network architecture for joint MRI reconstruction and segmentation with model-based deep learning and U-net. b. the reconstruction scheme using model-based deep learning for cartesian and wave-encodings.
Figure 5. Reconstructed images using SENSE, MoDL, wave-CAIPI and wave-MoDL at RyxRz=4x4 on 32-channel HCP data.