title: Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.
abstract: Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.
authors: IBBT-Vision Lab, Department of Physics, University of Antwerp, Belgium, 2610. jeny.rajan@ua.ac.be
authors: Jeny
authors: J
authors: Rajan
authors: Jelle
authors: J
authors: Veraart
authors: Johan
authors: J
authors: Van Audekerke
authors: Marleen
authors: M
authors: Verhoye
authors: Jan
authors: J
authors: Sijbers
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D005583:Fourier Analysis; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051379:Mice; D015233:Models, Statistical; D016011:Normal Distribution; D059629:Signal-To-Noise Ratio; D013269:Stochastic Processes