Using the GRAPPA operator and the generalized sampling theorem to reconstruct undersampled non-Cartesian data.
about
Three-dimensional through-time radial GRAPPA for renal MR angiography.Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data.RARE/turbo spin echo imaging with Simultaneous Multislice Wave-CAIPIRT-GROG: parallelized self-calibrating GROG for real-time MRI.Rotated stack-of-spirals partial acquisition for rapid volumetric parallel MRIWave-CAIPI for highly accelerated 3D imagingSelf-Calibrating Wave-Encoded Variable-Density Single-Shot Fast Spin Echo Imaging.K-t sparse GROWL: sequential combination of partially parallel imaging and compressed sensing in k-t space using flexible virtual coil.
P2860
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P2860
Using the GRAPPA operator and the generalized sampling theorem to reconstruct undersampled non-Cartesian data.
description
2009 nî lūn-bûn
@nan
2009 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի մարտին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Using the GRAPPA operator and ...... dersampled non-Cartesian data.
@ast
Using the GRAPPA operator and ...... dersampled non-Cartesian data.
@en
type
label
Using the GRAPPA operator and ...... dersampled non-Cartesian data.
@ast
Using the GRAPPA operator and ...... dersampled non-Cartesian data.
@en
prefLabel
Using the GRAPPA operator and ...... dersampled non-Cartesian data.
@ast
Using the GRAPPA operator and ...... dersampled non-Cartesian data.
@en
P2093
P50
P356
P1476
Using the GRAPPA operator and ...... ndersampled non-Cartesian data
@en
P2093
Felix A Breuer
Hisamoto Moriguchi
Mark A Griswold
Philipp Ehses
P304
P356
10.1002/MRM.21891
P577
2009-03-01T00:00:00Z