Joint-diffusion GRAPPA: enabling higher acceleration rates in dMRI by exploiting joint information from the k- and q-space

In this work, we generalize conventional GRAPPA-based dMRI reconstruction by exploiting joint information from the k-and q-space simultaneously. Higher acceleration in-plane rates than those commonly reported may be achieved when the missing k-space lines are learned using all information available in the whole k-space data set, that is, considering multi-coil channel information as well as the k-space data probed at different q-space points.

Our novel method, joint-diffusion GRAPPA, is validated with in-vivo multi-slice dMRI data, where we show it always outperforms conventional GRAPPA in terms of image quality, and works reasonably well for regimes where conventional GRAPPA results in significant noise penalty ($$$R_{in-plane}$$$ > 3 ).