Denoising sparse images from GRAPPA using the nullspace method (DESIGN)
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Adalsteinsson_Denoising sparse.pdf
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5.72 MB
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029303664a345c51a0d35fbf79213c31
Author(s)
Weller, Daniel S.
Polimeni, Jonathan R.
Grady, Leo
Wald, Lawrence L.
Adalsteinsson, Elfar
Goyal, Vivek K.
Alternative Title
Denoising sparse images from GRAPPA using the nullspace method
Date Issued
December 2011
Journal
Magnetic Resonance in Medicine
Publisher
Wiley Blackwell
Citation
Weller, Daniel S., Jonathan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, and Vivek K. Goyal. “Denoising Sparse Images from GRAPPA Using the Nullspace Method.” Magnetic Resonance Medicine 68, no. 4 (October 2012): 1176–1189.
Version
Author's final manuscript
Abstract
To accelerate magnetic resonance imaging using uniformly undersampled (nonrandom) parallel imaging beyond what is achievable with generalized autocalibrating partially parallel acquisitions (GRAPPA) alone, the DEnoising of Sparse Images from GRAPPA using the Nullspace method is developed. The trade-off between denoising and smoothing the GRAPPA solution is studied for different levels of acceleration. Several brain images reconstructed from uniformly undersampled k-space data using DEnoising of Sparse Images from GRAPPA using the Nullspace method are compared against reconstructions using existing methods in terms of difference images (a qualitative measure), peak-signal-to-noise ratio, and noise amplification (g-factors) as measured using the pseudo-multiple replica method. Effects of smoothing, including contrast loss, are studied in synthetic phantom data. In the experiments presented, the contrast loss and spatial resolution are competitive with existing methods. Results for several brain images demonstrate significant improvements over GRAPPA at high acceleration factors in denoising performance with limited blurring or smoothing artifacts. In addition, the measured g-factors suggest that DEnoising of Sparse Images from GRAPPA using the Nullspace method mitigates noise amplification better than both GRAPPA and L1 iterative self-consistent parallel imaging reconstruction (the latter limited here by uniform undersampling).
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1002/mrm.24116
