Proceedings Article10.1109/ICCA.2009.5410176
An improved GRAPPA algorithm based on sensitivity estimation
Ran Yang,Jingxin Zhang,Cishen Zhang +2 more
- 01 Dec 2009
- pp 1786-1791
TL;DR: New GRAPPA algorithm is developed where the above assumption is relaxed and the missing k-space data are reconstructed based on physical properties of k- space data and coil sensitivity profiles, which can be estimated using Auto-Calibrating Signal (ACS) lines.
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Abstract: This paper analyzes the famous GRAPPA algorithm, which is one of most widely used image reconstruction algorithms for parallel magnetic resonance imaging (pMRI). Inherently the existing GRAPPA type algorithms ignore the physical background of k-space data and treat the image reconstruction problem as a pure data interpolation problem which is solved based on an assumption that the k-space data are shift-invariant autoregressive process. Based on physical principles of MRI, this paper reveals the difficulty of such assumption. New GRAPPA algorithm is developed where the above assumption is relaxed and the missing k-space data are reconstructed based on physical properties of k-space data and coil sensitivity profiles, which can be estimated using Auto-Calibrating Signal (ACS) lines. This proposed algorithm can greatly improve the image quality even at very high acceleration factor. The in vivo examples demonstrate the overwhelming advantages of the proposed algorithm.
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Citations
An improved GRAPPA image reconstruction algorithm for parallel MRI
Chunli Wu,Wenjuan Hu,Ruwen Kan,Jianyu,Xiyan Sun +4 more
- 23 May 2011
TL;DR: The proposed FIR model has been a better description for the correlation of k-space data and a better approximation for the inversion of parallel imaging process and the results show that this improved algorithm can greatly improve the image quality even at very high acceleration factor.
2
Patent
Magnetic resonance parallel image acquisition and image reconstruction method
翟人宽
- 27 Dec 2011
TL;DR: In this paper, a magnetic resonance parallel image acquisition method is proposed, which comprises the steps that at least two fitting modules are set in an undersampled k space; structures of k space matrixes included in the fitting modules include the same; each fitting module internally comprises actually acquired k space data and data fitted by the actually acquired K space data; a coalescence coefficient is obtained by using two or more fitting modules; unders sampled data is calculated by using the coalescence coefficients; the undersampling space is filled; and a full sampled k space is formed.
Comparison of reconstruction and acquisition choices for quantitative T2* maps and synthetic contrasts.
TL;DR: Phase images have artifacts if reconstructed with a vendor’s sum of squares mode and Quantitative T2* values can be obtained from DICOM data instead of k-space data.
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SENSE: Sensitivity Encoding for fast MRI
TL;DR: The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k‐space sampling patterns and special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density.
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Mark A. Griswold,Peter M. Jakob,Robin M. Heidemann,Mathias Nittka,Vladimir Jellus,Jianmin Wang,Berthold Kiefer,Axel Haase +7 more
TL;DR: This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD‐AUTO‐SMASH reconstruction techniques and provides unaliased images from each component coil prior to image combination.
The NMR phased array.
TL;DR: Methods for simultaneously acquiring and subsequently combining data from a multitude of closely positioned NMR receiving coils are described, conceptually similar to phased array radar and ultrasound and hence the techniques are called the “NMR phased array.”
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Adaptive reconstruction of phased array NMR imagery
TL;DR: Experimental results indicate SNR performance approaching that of the optimal matched filter and the technique enables near‐optimal reconstruction of multicoil MR imagery without a‐priori knowledge of the individual coil field maps or noise correlation structure.