fRAKI: k-space deep learning with offline data-universal and online scan-specific priors

Abstract

Sampling a limited number of phase-encoding lines followed by estimating missing lines is an efficient method for shortening scan time of MRI. GeneRalized Autocalibarating Partial Parallel Acquisition (GRAPPA) is such a classical method and is widely used in clinical MRI. As a non-linear method, Robust Artificial-neural-networks for K-space Interpolation (RAKI) is a break-through of GRAPPA in the sense of much higher estimation accuracy. However, RAKI takes much longer estimation time because it requires online training a network for each receiving coil. To overcome the low-efficiency problem, we propose a fast version of RAKI (called fRAKI). fRAKI is roughly 26 times faster and can obtain much higher estimation accuracy compared with RAKI. The high efficiency of fRAKI is due to two properties: (1) A single network is shared to estimate missing lines of all the coils. (2) The online training of fRAKI can converge after a smaller number of iterations. Fast convergency is obtained by using a pre-trained model for initializing learnable parameters. High accuracy benefits from that the pre-train model contains data-universal prior and is also used as a sub-network of fRAKI so that the online training subnetwork can focus on learning scan-specific prior without the risk of overfitting the scan-specific data. Experimental results on the NYU fastMRI knee and brain datasets demonstrate the efficiency and accuracy of the proposed fRAKI.