Meeting Banner
Abstract #1982

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

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

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 RyxRz=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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here