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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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