  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 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.
