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Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction

By Daniel S. Weller, Jonathan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson and Vivek K. Goyal

Abstract

The amount of calibration data needed to produce images of adequate quality can prevent auto-calibrating parallel imaging reconstruction methods like generalized autocalibrating partially parallel acquisitions (GRAPPA) from achieving a high total acceleration factor. To improve the quality of calibration when the number of auto-calibration signal (ACS) lines is restricted, we propose a sparsity-promoting regularized calibration method that finds a GRAPPA kernel consistent with the ACS fit equations that yields jointly sparse reconstructed coil channel images. Several experiments evaluate the performance of the proposed method relative to unregularized and existing regularized calibration methods for both low-quality and underdetermined fits from the ACS lines. These experiments demonstrate that the proposed method, like other regularization methods, is capable of mitigating noise amplification, and in addition, the proposed method is particularly effective at minimizing coherent aliasing artifacts caused by poor kernel calibration in real data. Using the proposed method, we can increase the total achievable acceleration while reducing degradation of the reconstructed image better than existing regularized calibration methods.National Science Foundation (U.S.) (CAREER Grant 0643836)National Institutes of Health (U.S.) (Grant NIH R01 EB007942)National Institutes of Health (U.S.) (Grant NIH R01 EB006847)National Institutes of Health (U.S.) (Grant NIH P41 RR014075)National Institutes of Health (U.S.) (Grant NIH K01 EB011498)National Institutes of Health (U.S.) (Grant NIH F32 EB015914)National Science Foundation (U.S.). Graduate Research Fellowship Progra

Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Year: 2013
DOI identifier: 10.1109/TMI.2013.2256923
OAI identifier: oai:dspace.mit.edu:1721.1/85875
Provided by: DSpace@MIT
Journal:

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