Journal ArticleDOI
Effective noise estimation and filtering from correlated multiple-coil MR data.
TLDR
It is shown, through a number of experiments with synthetic, phantom, and in vivo data, that neglecting the correlated nature of noise in multiple-coil systems implies important errors even in the simplest cases and the proper statistical characterization of noise through effective parameters drives to improved accuracy for both of the problems studied.Citations
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Journal ArticleDOI
Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters.
TL;DR: Following a Bayesian modeling approach, a generalized total variation-based MRI denoising model is proposed based on global hyper-Laplacian prior and Rician noise assumption and has the properties of backward diffusion in local normal directions and forward diffusion inLocal tangent directions.
Journal ArticleDOI
Noise estimation in parallel MRI: GRAPPA and SENSE
TL;DR: This paper proposes a novel methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images.
BookDOI
Statistical Analysis of Noise in MRI
TL;DR: The first € price and the £ and $ price are net prices, subject to local VAT, and the €(D) includes 7% for Germany, the€(A) includes 10% for Austria.
Journal ArticleDOI
Data distributions in magnetic resonance images: a review.
TL;DR: This review paper provides an overview of the various distributions that occur when dealing with MR data, considering both single-coil and multiple- coil acquisition systems and summarizes how knowledge of the MR data distributions can be used to construct optimal parameter estimators.
Journal ArticleDOI
Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization
Erick J. Canales-Rodríguez,Alessandro Daducci,Stamatios N. Sotiropoulos,Emmanuel Caruyer,Santiago Aja-Fernández,Joaquim Radua,Jesús M. Yurramendi Mendizabal,Yasser Iturria-Medina,Lester Melie-Garcia,Yasser Alemán-Gómez,Jean-Philippe Thiran,Salvador Sarró,Edith Pomarol-Clotet,Raymond Salvador +13 more
TL;DR: A Robust and Unbiased Model-BAsed Spherical Deconvolution technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models is developed.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
A Review of Image Denoising Algorithms, with a New One
TL;DR: A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
Journal ArticleDOI
The rician distribution of noisy mri data
Hakon Gudbjartsson,Samuel Patz +1 more
TL;DR: The image intensity in magnetic resonance magnitude images in the presence of noise is shown to be governed by a Rician distribution and low signal intensities (SNR < 2) are therefore biased due to the noise.
Journal ArticleDOI
The NMR phased array.
TL;DR: Methods for simultaneously acquiring and subsequently combining data from a multitude of closely positioned NMR receiving coils are described, conceptually similar to phased array radar and ultrasound and hence the techniques are called the “NMR phased array.”
Journal ArticleDOI
Design and construction of a realistic digital brain phantom
D. L. Collins,Alex P. Zijdenbos,V. Kollokian,John G. Sled,Noor Jehan Kabani,Colin J. Holmes,Alan C. Evans +6 more
TL;DR: The authors present a realistic, high-resolution, digital, volumetric phantom of the human brain, which can be used to simulate tomographic images of the head and is the ideal tool to test intermodality registration algorithms.