MRI data is acquired in Fourier space/k-space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by providing fast and more efficient data acquisition, and improving motion robustness. Nonetheless, the image reconstruction process of non-Cartesian data is more involved and can be time-consuming, even through the use of efficient algorithms such as non-uniform FFT (NUFFT). Reconstruction complexity is further exacerbated when imaging in the presence of field imperfections. This work (implicit GROG) provides an efficient approach to transform the field corrupted non-Cartesian data into clean Cartesian data, to achieve simpler and faster reconstruction which should help enable non-Cartesian data sampling to be performed more widely in MRI.
@article{arxiv.2310.10823,
title = {Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction},
author = {Daniel Abraham and Mark Nishimura and Xiaozhi Cao and Congyu Liao and Kawin Setsompop},
journal= {arXiv preprint arXiv:2310.10823},
year = {2024}
}