Accelerated parallel magnetic resonance imaging reconstruction using joint estimation with a sparse signal model
Author(s)Weller, Daniel S.; Polimeni, Jonathan R.; Grady, Leo; Wald, Lawrence L.; Adalsteinsson, Elfar; Goyal, Vivek K.; ... Show more Show less
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Accelerating magnetic resonance imaging (MRI) by reducing the number of acquired k-space scan lines benefits conventional MRI significantly by decreasing the time subjects remain in the magnet. In this paper, we formulate a novel method for Joint estimation from Undersampled LinEs in Parallel MRI (JULEP) that simultaneously calibrates the GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) reconstruction kernel and reconstructs the full multi-channel k-space. We employ a joint sparsity signal model for the channel images in conjunction with observation models for both the acquired data and GRAPPA reconstructed k-space. We demonstrate using real MRI data that JULEP outperforms conventional GRAPPA reconstruction at high levels of undersampling, increasing the peak-signal-to-noise ratio by up to 10 dB.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Research Laboratory of Electronics
Proceedings of the 2012 IEEE Statistical Signal Processing Workshop (SSP)
Institute of Electrical and Electronics Engineers (IEEE)
Weller, Daniel S., Jonathan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, and Vivek K Goyal. “Accelerated Parallel Magnetic Resonance Imaging Reconstruction Using Joint Estimation with a Sparse Signal Model.” 2012 IEEE Statistical Signal Processing Workshop (SSP) (n.d.).
Author's final manuscript