Accelerated Dynamic Imaging by Reconstructing Sparse Differences using Compressed Sensing
نویسندگان
چکیده
Introduction Dynamic imaging with high spatial and temporal resolution is a demanding task in clinical MR tomography. In case of undersampling in dynamic imaging, radial trajectories are advantageous due to their incoherent artifact behavior. Compressed Sensing (CS) [1,2] is a new technique for reconstructing accelerated datasets without utilizing parallel imaging methods. First applications of CS in the field of MR have been demonstrated [3,4]. CS reconstructs missing data points by optimizing a mathematical functional and depends on a sparse representation (in any basis) of the desired signal.
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