RT-GROG: parallelized self-calibrating GROG for real-time MRI.
A real-time implementation of self-calibrating Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) operator gridding for radial acquisitions is presented. Self-calibrating GRAPPA operator gridding is a parallel-imaging-based, parameter-free gridding algorithm, where coil sensitivity profiles are used to calculate gridding weights. Self-calibrating GRAPPA operator gridding's weight-set calculation and image reconstruction steps are decoupled into two distinct processes, implemented in C++ and parallelized. This decoupling allows the weights to be updated adaptively in the background while image reconstruction threads use the most recent gridding weights to grid and reconstruct images. All possible combinations of two-dimensional gridding weights G(x)(m)G(y)(n) are evaluated for m,n = {-0.5, -0.4, ..., 0, 0.1, ..., 0.5} and stored in a look-up table. Consequently, the per-sample two-dimensional weights calculation during gridding is eliminated from the reconstruction process and replaced by a simple look-up table access. In practice, up to 34x faster reconstruction than conventional (parallelized) self-calibrating GRAPPA operator gridding is achieved. On a 32-coil dataset of size 128 x 64, reconstruction performance is 14.5 frames per second (fps), while the data acquisition is 6.6 fps.
Saybasili
Haris
H
Translational Medicine Branch, National Institutes of Health/National Heart, Lung and Blood Institute (NHLBI), Department of Health and Human Services (DHHS), Bethesda, Maryland 20892-1061, USA. saybasilih@nhlbi.nih.gov
Derbyshire
J Andrew
JA
Kellman
Peter
P
Griswold
Mark A
MA
Ozturk
Cengizhan
C
Lederman
Robert J
RJ
Seiberlich
Nicole
N
D000465:Algorithms; D002138:Calibration; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging