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Abstract #0103

Hierarchically Semiseparable Generalized Encoding Matrix Compression for Fast Distortion Corrected Inverse Imaging

Stephen F Cauley 1,2 , Kawin Setsompop 1,2 , Dan Ma 3 , Yun Jiang 3 , Elfar Adalsteinsson 4 , Lawrence Wald 1,2 , and Mark Griswold 3,5

1 Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, United States, 2 Dept. of Radiology, Harvard Medical School, Boston, MA, United States, 3 Dept. of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, 4 Harvard-MIT Div. of Health Sci. and Tech., Dept. of Electrical Engineering and Computer Science, Cambridge, MA, United States, 5 Dept. of Radiology, Case Western Reserve University and University Hospitals of Cleveland, Cleveland, Ohio, United States

Reconstruction of non-Cartesian data can be a computationally demanding problem. Iterative numerical solutions often involve repeated evaluation of Discrete Fourier or NUFT operators, coil sensitivity profiles, and other physical MR parameters. Alternatively, Hierarchically Semiseparable (HSS) modeling can be used to compute an approximate inverse of the generalized encoding matrix. The HSS model can be computed prior to data collection and is ideal for time series reconstruction, e.g. fMRI, cardiac imaging, and MR fingerprinting. We demonstrate a 40x speed-up when compared to state-of-the-art iterative solvers for the reconstruction of distortion corrected spiral data.

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