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

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