eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
This work addresses computational inefficiencies in MRI reconstruction for medical imaging, representing an incremental improvement with specific gains.
The paper tackled the computational burden of RAKI for MRI reconstruction by accelerating it over 200 times through coil combination and joint reconstruction, and applied this to rapidly obtain T2 and T2* parameter maps from fast EPTI scans.
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.