Abstract #3341

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

Gabriel Ramos-Llordén¹, Santiago Aja-Fernández², Congyu Liao³, Kawin Setsompop³, and Yogesh Rathi¹

¹Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, ²Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain, ³Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States

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 ).

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