@article {1438,
	title = {Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.},
	journal = {Magnetic resonance imaging},
	volume = {30},
	year = {2012},
	month = {2012 Dec},
	pages = {1512-8},
	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.},
	keywords = {Algorithms, Animals, Brain, Brain Mapping, Computer Simulation, Fourier Analysis, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Mice, Models, Statistical, Normal Distribution, Signal-To-Noise Ratio, Stochastic Processes},
	issn = {1873-5894},
	doi = {10.1016/j.mri.2012.04.021},
	author = {Jeny Rajan and Jelle Veraart and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@article {1189,
	title = {Noise measurement from magnitude MRI using local estimates of variance and skewness.},
	journal = {Physics in medicine and biology},
	volume = {55},
	year = {2010},
	month = {2010 Aug 21},
	pages = {N441-9},
	abstract = {In this note, we address the estimation of the noise level in magnitude magnetic resonance (MR) images in the absence of background data. Most of the methods proposed earlier exploit the Rayleigh distributed background region in MR images to estimate the noise level. These methods, however, cannot be used for images where no background information is available. In this note, we propose two different approaches for noise level estimation in the absence of the image background. The first method is based on the local estimation of the noise variance using maximum likelihood estimation and the second method is based on the local estimation of the skewness of the magnitude data distribution. Experimental results on synthetic and real MR image datasets show that the proposed estimators accurately estimate the noise level in a magnitude MR image, even without background data.},
	keywords = {Algorithms, Artifacts, Brain, Data Interpretation, Statistical, Fourier Analysis, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Magnetic Resonance Imaging, Models, Statistical, Myocardium, Normal Distribution, Reproducibility of Results},
	issn = {1361-6560},
	doi = {10.1088/0031-9155/55/16/N02},
	author = {Jeny Rajan and Dirk H J Poot and Juntu, Jaber and Jan Sijbers}
}
@article {1374,
	title = {Quantitative diffusion tensor imaging in amyotrophic lateral sclerosis: revisited.},
	journal = {Human brain mapping},
	volume = {30},
	year = {2009},
	month = {2009 Nov},
	pages = {3657-75},
	abstract = {Voxel-based analyses (VBA) are increasingly being used to detect white matter abnormalities with diffusion tensor imaging (DTI) in different types of pathologies. However, the validity, specificity, and sensitivity of statistical inferences of group differences to a large extent depend on the quality of the spatial normalization of the DTI images. Using high-dimensional nonrigid coregistration techniques that are able to align both the spatial and orientational diffusion information and incorporate appropriate templates that contain this complete DT information may improve this quality. Alternatively, a hybrid technique such as tract-based spatial statistics (TBSS) may improve the reliability of the statistical results by generating voxel-wise statistics without the need for perfect image alignment and spatial smoothing. In this study, we have used (1) a coregistration algorithm that was optimized for coregistration of DTI data and (2) a population-based DTI atlas to reanalyze our previously published VBA, which compared the fractional anisotropy and mean diffusivity maps of patients with amyotrophic lateral sclerosis (ALS) with those of healthy controls. Additionally, we performed a complementary TBSS analysis to improve our understanding and interpretation of the VBA results. We demonstrate that, as the overall variance of the diffusion properties is lowered after normalizing the DTI data with such recently developed techniques (VBA using our own optimized high-dimensional nonrigid coregistration and TBSS), more reliable voxel-wise statistical results can be obtained than had previously been possible, with our VBA and TBSS yielding very similar results. This study provides support for the view of ALS as a multisystem disease, in which the entire frontotemporal lobe is implicated.},
	keywords = {Adult, Aged, Algorithms, Amyotrophic Lateral Sclerosis, anisotropy, Brain, Brain Mapping, Case-Control Studies, diffusion tensor imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Reproducibility of Results, Sensitivity and Specificity, Statistics as Topic},
	issn = {1097-0193},
	doi = {10.1002/hbm.20794},
	author = {Caroline A Sage and Wim Van Hecke and Ron R Peeters and Jan Sijbers and Robberecht, Wim and Paul M Parizel and Marchal, Guy and Alexander Leemans and Stefan Sunaert}
}
