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    Accelerated parallel magnetic resonance imaging reconstruction using joint estimation with a sparse signal model

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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.National Science Foundation (U.S.) (CAREER Grant 0643836)National Center for Research Resources (U.S.) (P41 RR014075)National Institutes of Health (U.S.) (NIH R01 EB007942)National Institutes of Health (U.S.) (NIH R01 EB006847)Siemens CorporationNational Science Foundation (U.S.). Graduate Research Fellowship Progra
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