Reconstruction of phase images for GRAPPA accelerated Magnetic Resonance Imaging
ABSTRACT In this work we present a method to combine complex-valued phased array MR data based on a uniform sensitivity approach, which
incorporates sensitivity profiles calculated from afore acquired data. The algorithm was implemented on a clinical 3T whole-body
MR-Scanner and embedded into the vendor-specific standard reconstruction chain.
Additionally, it was linked with the GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) procedure to take advantage
of partial parallel acquisition techniques as well. Thus, under-sampled data can be reconstructed with GRAPPA and subsequently
combined with the presented method. Phase images reconstructed with the proposed method were in excellent agreement with reference
data and did not suffer from errors due to incorrectly combined data.
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ABSTRACT: Magnetic susceptibility is an intrinsic tissue property that recently became measureable in vivo by a magnetic-resonance based technique called quantitative susceptibility mapping (QSM). Although QSM may be performed without additional acquisition time, for example, in the course of the well-established susceptibility weighted imaging, the applicability of QSM is currently hampered by the numerical complexity and computational cost associated with the reconstruction procedure. This work introduces a novel QSM framework called superfast dipole inversion which allows rapid online reconstruction of susceptibility maps from wrapped raw gradient-echo phase data. The algorithm relies on the extension and combination of several recent algorithms involving the precalculation of convolution kernels and the correction of inversion artifacts. Reconstruction of three-dimensional high resolution susceptibility maps of the human brain was achieved with superfast dipole inversion in less than 20 s on a conventional workstation computer. Thus, superfast dipole inversion opens the door to an implementation of QSM on MR scanner hardware as well as to the routine reconstruction of large cohorts of datasets. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.Magnetic Resonance in Medicine 07/2012; · 3.27 Impact Factor