Calibrationless oscar-based image reconstruction in compressed sensing parallel MRI

Loubna El Gueddari 1, 2 Philippe Ciuciu 1, 2 Emilie Chouzenoux 3, 4 Alexandre Vignaud 1 Jean-Christophe Pesquet 3, 4
1 NEUROSPIN - Service NEUROSPIN
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
3 CVN - Centre de vision numérique
4 OPIS - OPtimisation Imagerie et Santé
Inria Saclay - Ile de France, CVN - Centre de vision numérique
Abstract : Reducing acquisition time is a crucial issue in MRI especially in the high resolution context. Compressed sensing has faced this problem for a decade. However, to maintain a high signal-to-noise ratio (SNR), CS must be combined with parallel imaging. This leads to harder reconstruction problems that usually require the knowledge of coil sensitivity profiles. In this work, we introduce a calibra-tionless image reconstruction approach that no longer requires this knowledge. The originality of this work lies in using for reconstruction a group sparsity structure (called OSCAR) across channels that handles SNR inhomogeneities across receivers. We compare this reconstruction with other calibrationless approaches based on group-LASSO and its sparse variation as well as with the auto-calibrated method called 1-ESPIRiT. We demonstrate that OSCAR outper-forms its competitors and provides similar results to 1-ESPIRiT. This suggests that the sensitivity maps are no longer required to perform combined CS and parallel imaging reconstruction.
Keywords : Compressed Sensing Proximal algorithm Group Sparsity Parallel MRI
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Conference papers
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Submitted on : Tuesday, April 16, 2019 - 3:53:27 PM
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Loubna El Gueddari, Philippe Ciuciu, Emilie Chouzenoux, Alexandre Vignaud, Jean-Christophe Pesquet. Calibrationless oscar-based image reconstruction in compressed sensing parallel MRI. ISBI 2019 - IEEE International Symposium on Biomedical Imaging, Apr 2019, Venise, Italy. ⟨hal-02101262⟩

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