Fast GPU Implementation of a Scan-Specific Deep Learning Reconstruction for Accelerated Magnetic Resonance Imaging

RAKI is a novel fast MRI image reconstruction algorithm that has been recently proposed, which gives satisfying results for highly accelerated MRI. However, due to RAKI reconstruction depends on multiple convolutional neural networks, implementing RAKI reconstruction is a time-consuming task. In this study, we present accelerate strategies for RAKI implementation aided by GPU parallel programming. Aiming at the characteristics of RAKI, we limited the iteration number of solving optimization problems in the network training stage, while maintaining the reconstruction results are visually satisfying. Further more, according to the independence between multiple networks, we parallelized the training tasks by CPU multiprocessing, which maximizes the performance by fully utilizing GPU resources. According to our experiments, these efforts gave more than 60x speed up compared with conventional, sequential implementation. With the ability of completing RAKI reconstruction in minutes, we are able to bring RAKI into practical applications.