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HYR2PICS: Hybrid Regularized Reconstruction for combined Parallel Imaging and Compressive Sensing in MRI

Abstract : Both parallel Magnetic Resonance Imaging~(pMRI) and Compressed Sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data in the $k$-space. So far, first attempts to combine sensitivity encoding (SENSE) imaging in pMRI with CS have been proposed in the context of Cartesian trajectories. Here, we extend these approaches to non-Cartesian trajectories by jointly formulating the CS and SENSE recovery in a hybrid Fourier/wavelet framework and optimizing a convex but nonsmooth criterion. On anatomical MRI data, we show that HYR$^2$PICS outperforms wavelet-based regularized SENSE reconstruction. Our results are also in agreement with the Transform Point Spread Function (TPSF) criterion that measures the degree of incoherence of $k$-space undersampling schemes.
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Contributor : Philippe Ciuciu <>
Submitted on : Thursday, April 26, 2012 - 4:48:34 PM
Last modification on : Thursday, March 5, 2020 - 5:55:56 PM
Long-term archiving on: : Monday, November 26, 2012 - 3:51:07 PM

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  • HAL Id : cea-00691623, version 1

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Claire Boyer, Philippe Ciuciu, Pierre Weiss, Sébastien Meriaux. HYR2PICS: Hybrid Regularized Reconstruction for combined Parallel Imaging and Compressive Sensing in MRI. IEEE International Symposium on Biomedical Imaging, May 2012, Barcelone, Spain. ⟨cea-00691623⟩

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