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Abstract #0379

L1 SPIR-IT: Autocalibrating Parallel Imaging Compressed Sensing

Michael Lustig1, Marcus Alley2, Shreyas Vasanawala2, David L. Donoho3, John Mark Pauly1

1Electrical Engineering, Stanford University, Stanford, CA, USA; 2Radiology, Stanford University; 3Statistics, Stanford University

A detailed approach of combining auto-calibrating parallel imaging (acPI) with compressed sensing (CS) is presented. The acquisition and the reconstruction are carefully optimized to meet the requirements of both methods in order to achieve highly accelerated robust reconstructions. Poisson-disc sampling distribution is used to achieve the required incoherency for CS and uniform density for acPI. A novel L1-wavelet penalized, iterative reconstruction (L1 SPIR-iT) is used to enforce consistency with the calibration and data acquisition, and in addition, joint sparsity of the reconstructed coil images. High quality in vivo, 5-fold accelerated reconstruction using only 4 coils is demonstrated.