Abstract #2402

System conditioning during GRAPPA kernel training improves temporal SNR in accelerated EPI-based functional, diffusion, and perfusion MRI applications

W Scott Hoge^1,2,3 and Jonathan R Polimeni^2,3,4

¹Radiology, Brigham and Women's Hospital, Boston, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

This work examines methods to improve the conditioning of the linear system of equations used to compute GRAPPA and Dual-Polarity GRAPPA reconstruction coefficients, and it's effect on temporal SNR in applications that employ accelerated EPI data. We test three methods: (i) system normalization, (ii) simple Tikhonov regularization, and (iii) 2D k-space filters applied to the calibration data prior to the linear system formation. Examples of tSNR improvement are shown, drawing from EPI-based in-vivo functional, diffusion, and perfusion imaging data acquired at 3T and 7T.

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