Abstract #0273

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

Heng Yu¹, Zijing Dong^2,3, Yamin Arefeen², Congyu Liao⁴, Kawin Setsompop^4,5, and Berkin Bilgic^3,6,7

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.

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