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k ‐ t GRAPPA: A k ‐space implementation for dynamic MRI with high reduction factor
Author(s) -
Huang Feng,
Akao James,
Vijayakumar Sathya,
Duensing George R.,
Limkeman Mark
Publication year - 2005
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.20641
Subject(s) - k space , computer science , magnetic resonance imaging , interpolation (computer graphics) , reduction (mathematics) , algorithm , dimension (graph theory) , missing data , imaging phantom , dynamic imaging , temporal resolution , sliding window protocol , image resolution , nuclear magnetic resonance , artificial intelligence , physics , mathematics , window (computing) , mathematical analysis , fourier transform , optics , image processing , image (mathematics) , geometry , medicine , digital image processing , machine learning , pure mathematics , radiology , operating system
A novel technique called “ k ‐ t GRAPPA” is introduced for the acceleration of dynamic magnetic resonance imaging. Dynamic magnetic resonance images have significant signal correlations in k ‐space and time dimension. Hence, it is feasible to acquire only a reduced amount of data and recover the missing portion afterward. Generalized autocalibrating partially parallel acquisitions (GRAPPA), as an important parallel imaging technique, linearly interpolates the missing data in k ‐space. In this work, it is shown that the idea of GRAPPA can also be applied in k ‐ t space to take advantage of the correlations and interpolate the missing data in k ‐ t space. For this method, no training data, filters, additional parameters, or sensitivity maps are necessary, and it is applicable for either single or multiple receiver coils. The signal correlation is locally derived from the acquired data. In this work, the k ‐ t GRAPPA technique is compared with our implementation of GRAPPA, TGRAPPA, and sliding window reconstructions, as described in Methods. The experimental results manifest that k ‐ t GRAPPA generates high spatial resolution reconstruction without significant loss of temporal resolution when the reduction factor is as high as 4. When the reduction factor becomes higher, there might be a noticeable loss of temporal resolution since k ‐ t GRAPPA uses temporal interpolation. Images reconstructed using k ‐ t GRAPPA have less residue/folding artifacts than those reconstructed by sliding window, much less noise than those reconstructed by GRAPPA, and wider temporal bandwidth than those reconstructed by GRAPPA with residual k ‐space. k ‐ t GRAPPA is applicable to a wide range of dynamic imaging applications and is not limited to imaging parts with quasi‐periodic motion. Since only local information is used for reconstruction, k ‐ t GRAPPA is also preferred for applications requiring real time reconstruction, such as monitoring interventional MRI. Magn Reson Med, 2005. © 2005 Wiley‐Liss, Inc.