title: Parallel MRI noise correction: an extension of the LMMSE to non central chi distributions.
abstract: Parallel MRI leads to magnitude data corrupted by noise described in most cases as following a Rician or a non central chi distribution. And yet, very few correction methods perform a non central chi noise removal. However, this correction step, adapted to the correct noise model, is of very much importance, especially when working with Diffusion Weighted MR data yielding a low SNR. We propose an extended Linear Minimum Mean Square Error estimator (LMMSE), which is adapted to deal with non central chi distributions. We demonstrate on simulated and real data that the extended LMMSE outperforms the original LMMSE on images corrupted by a non central chi noise.
authors: CEA I2BM NeuroSpin, Gif-sur-Yvette, France.
authors: Véronique
authors: V
authors: Brion
authors: Cyril
authors: C
authors: Poupon
authors: Olivier
authors: O
authors: Riff
authors: Santiago
authors: S
authors: Aja-Fernández
authors: Antonio
authors: A
authors: Tristán-Vega
authors: Jean-François
authors: JF
authors: Mangin
authors: Denis
authors: D
authors: Le Bihan
authors: Fabrice
authors: F
authors: Poupon
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted