Abstract #1982

Wave-Encoded Model-Based Deep Learning with Joint Reconstruction and Segmentation

Jaejin Cho^1,2, Qiyuan Tian^1,2, Robert Frost^1,2, Itthi Chatnuntawech³, and Berkin Bilgic^1,2,4

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

We propose a wave-encoded model-based deep learning (wave-MoDL) strategy for simultaneous image reconstruction and segmentation. We use CNN-based regularizers in both k- and image-space to employ features in both domains and successfully incorporated wave-encoding strategy into MoDL reconstruction. Wave-MoDL enables R_yxR_z=4x4-fold accelerated 3D imaging using a 32-channel array while reducing NRMSE by 1.45-fold compared to wave-CAIPI. Further, we jointly train wave-MoDL and a U-net for simultaneous reconstruction and segmentation to get additional gain.

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