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K‐space reconstruction with anisotropic kernel support (KARAOKE) for ultrafast partially parallel imaging
Author(s) -
Miao Jun,
Wong Wilbur C. K.,
Narayan Sreenath,
Wilson David L.
Publication year - 2011
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3651693
Subject(s) - kernel (algebra) , anisotropy , imaging phantom , gaussian function , iterative reconstruction , artificial intelligence , computer science , image quality , k space , mathematics , algorithm , physics , computer vision , nuclear magnetic resonance , optics , gaussian , mathematical analysis , image (mathematics) , fourier transform , combinatorics , quantum mechanics
Purpose: Partially parallel imaging (PPI) greatly accelerates MR imaging by using surface coil arrays and under‐sampling k‐space. However, the reduction factor ( R ) in PPI is theoretically constrained by the number of coils ( N C ). A symmetrically shaped kernel is typically used, but this often prevents even the theoretically possible R from being achieved. Here, the authors propose a kernel design method to accelerate PPI faster than R = N C .Methods: K‐space data demonstrates an anisotropic pattern that is correlated with the object itself and to the asymmetry of the coil sensitivity profile, which is caused by coil placement and B 1 inhomogeneity. From spatial analysis theory, reconstruction of such pattern is best achieved by a signal‐dependent anisotropic shape kernel. As a result, the authors propose the use of asymmetric kernels to improve k‐space reconstruction. The authors fit a bivariate Gaussian function to the local signal magnitude of each coil, then threshold this function to extract the kernel elements. A perceptual difference model (Case‐PDM) was employed to quantitatively evaluate image quality.Results: A MR phantom experiment showed that k‐space anisotropy increased as a function of magnetic field strength. The authors tested a K‐spAce Reconstruction with AnisOtropic KErnel support (“KARAOKE”) algorithm with both MR phantom and in vivo data sets, and compared the reconstructions to those produced by GRAPPA, a popular PPI reconstruction method. By exploiting k‐space anisotropy, KARAOKE was able to better preserve edges, which is particularly useful for cardiac imaging and motion correction, while GRAPPA failed at a high R near or exceeding N C . KARAOKE performed comparably to GRAPPA at low R s.Conclusions: As a rule of thumb, KARAOKE reconstruction should always be used for higher quality k‐space reconstruction, particularly when PPI data is acquired at high R s and/or high field strength.