Meeting Banner
Abstract #0273

eRAKI: Fast Robust Artificial neural networks for K‐space Interpolation (RAKI) with Coil Combination and Joint Reconstruction

Heng Yu1, Zijing Dong2,3, Yamin Arefeen2, Congyu Liao4, Kawin Setsompop4,5, and Berkin Bilgic3,6,7
1Department of Automation, Tsinghua University, Beijing, China, 2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 3Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 4Radiological Sciences Laboratory, Stanford University, Stanford, CA, United States, 5Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 6Harvard Medical School, Boston, MA, United States, 7Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan. As it trains a separate model for each individual coil, learning and inference with RAKI can be computationally prohibitive, particularly for large 3D datasets. In this abstract, we accelerate RAKI by more than 200 times by directly learning a coil-combined target and further improve the reconstruction performance using joint reconstruction across multiple echoes together with an elliptical-CAIPI sampling approach. We further deploy these improvements in quantitative imaging and rapidly obtain T2 and T2* parameter maps from a fast EPTI scan.

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

Join Here