A CUDA-based reverse gridding algorithm for MR reconstruction

MR raw data collected using non-Cartesian method can be transformed on Cartesian grids by traditional gridding algorithm (GA) and reconstructed by Fourier transform. However, its runtime complexity is O(K× N2), where resolution of raw data is N× N and size of convolution window (CW) is K. And it involves a large number of matrix calculation including modulus, addition, multiplication and convolution. Therefore, a Compute Unified Device Architecture (CUDA)-based algorithm is proposed to improve the reconstruction efficiency of PROPELLER (a globally recognized non-Cartesian sampling method). Experiment shows a write–write conflict among multiple CUDA threads. This induces an inconsistent result when synchronously convoluting multiple k-space data onto the same grid. To overcome this problem, a reverse gridding algorithm (RGA) was developed. Different from the method of generating a grid window for each trajectory as in traditional GA, RGA calculates a trajectory window for each grid. This is what “reverse” means. For each k-space point in the CW, contribution is cumulated to this grid. Although this algorithm can be easily extended to reconstruct other non-Cartesian sampled raw data, we only implement it based on PROPELLER. Experiment illustrates that this CUDA-based RGA has successfully solved the write–write conflict and its reconstruction speed is 7.5 times higher than that of traditional GA.