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Abstract #3547

Self-calibrating nonlinear MR image reconstruction algorithms for variable density sampling and parallel imaging

Loubna EL GUEDDARI1, Carole LAZARUS1, HanaƩ CARRIE1, Alexandre VIGNAUD2, and Philippe CIUCIU1

1CEA/NeuroSpin & INRIA Parietal, Gif-sur-Yvette, France, 2CEA/NeuroSpin, Gif-sur-Yvette, France

Compressed Sensing has allowed a significant reduction of acquisition times in MRI. However, to maintain high signal-to-noise ratio during acquisition, CS is usually combined with parallel imaging (PI). Here, we propose a new self-calibrating MRI reconstruction framework that handles non-Cartesian CS and PI. Sensitivity maps are estimated from the data in the center of k-space while MR images are iteratively reconstructed by minimizing a nonsmooth criterion using the proximal optimized gradient method, which converges faster than FISTA. Comparison with L1-ESPIRiT suggests that our approach performs better both visually and numerically on 8-fold accelerated Human brain data collected at 7 Tesla.

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