High-Performance FPGA-based Accelerator using Random Projection for pMRI method

The random projection method reduces the dimensionality of the data to provide attractive computational advantages in the collection and processing of high-dimensional signals. In literature, it has been successfully applied for the pMRI method i.e., GRAPPA. This paper introduces a very sparse random projection matrix with the same statistical efficiency as dense matrix. The proposed method is implemented on FPGA (working with on-chip processor) device to speed up the GRAPPA reconstruction process. The reconstruction results for in vivo 30 channel human cardiac data set show 6x speed up with little loss in accuracy.