Self-calibrating nonlinear reconstruction algorithms for variable density sampling and parallel reception MRI

Abstract : Compressed Sensing has allowed a significant reduction of acquisition times in MRI, especially in the high resolution (e.g., 400 µm) context. However, in this setting CS must be combined with parallel reception as multichannel coil acquisitions maintain high input signal-to-noise ratio (SNR). To get rid of usual parallel imaging limitations (output SNR loss), non-Cartesian trajectories provide a gain in sampling efficiency in the CS context. In this paper, we propose a self-calibrating MRI reconstruction framework that handles variable density sampling. Low resolution sensitivity maps are estimated from the low frequency k-space content using an original and fast method while MR images are reconstructed using a nonlinear iterative algorithm, which promotes sparsity in the wavelet domain. As regards the optimization task, we compare three first-order prox-imal gradient methods (FB, FISTA, POGM) and evaluate their respective convergence speed. Comparison with state-of-the-art (i.e., 1-ESPIRiT) suggests that our self-calibrating POGM-based algorithm outperforms current approaches both in terms of image quality and computing time on prospectively accelerated ex-vivo and in-vivo data collected at 7 Tesla and we will focus more specifically on prospective non-Cartesian 8-fold accelerated in vivo Human brain data.
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https://hal.inria.fr/hal-01782428
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Loubna El Gueddari, C. Lazarus, H Carrié, A. Vignaud, Ph Ciuciu. Self-calibrating nonlinear reconstruction algorithms for variable density sampling and parallel reception MRI. 10th IEEE Sensor Array and Multichannel Signal Processing workshop, Jul 2018, Sheffield, United Kingdom. pp.1-5. ⟨hal-01782428v2⟩

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