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Abstract #2441

Rapid Parallel MRI with Convolution-based Reconstruction (CORE) and Deblurring by Compressed Sensing

Efrat Shimron1, Andrew G. Webb2, and Haim Azhari1

1Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel, 2Department of Radiology, Leiden University Medical Center (LUMC), Leiden, Netherlands

Methods combining Compressed Sensing (CS) and Parallel MRI (PI) for accelerated MRI have shown great promise, yet they are commonly hindered by heavy iterative computations. This work introduces the novel CORE-Deblur method for accelerated MRI, which integrates CS and PI and offers fast computations with very few iterations. CORE-Deblur utilizes the recently introduced CORE-PI technique and introduces the novel concept of using CS for image deblurring. Experiments with in-vivo data show that for highly subsampled k-space (R=5) CORE-Deblur reduces the number of CS iterations by 10-fold (from 95 to about 5-7) and improves the reconstruction accuracy by 5%-8%.

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