Regression Models for Identifying Noise Sources in Magnetic Resonance Images
نویسندگان
چکیده
منابع مشابه
Regression Models for Identifying Noise Sources in Magnetic Resonance Images.
Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magneti...
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This report summarizes the work I did within the Odyssée research group, in INRIA Sophia-Antipolis, under the supervision of Rachid Deriche. We have been working on problems related to noise in medical images, more specifically in diffusion weighted MRI, originating from physical process we are able to model. In the light of these models, the purpose of our work was to evaluate existing reconst...
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The objective of this paper is to do the estimation of the noise in the Magnetic Resonance images and evaluate the noise reduction algorithm present in this paper. We propose a method for reduction of Rician noise in MRI. This method shows an optimal estimation result that is more accurate in recovering the true signal from Rician noise. The method proposed specifically for Rician noise reducti...
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Many image processing applications within MRI are grounded on stochastic methods based on the prior knowledge on the statistics of noise. The ubiquitous Gaussian model provides a poor fitting for mediumlow SNRs, yielding to the use of Rician statistics: the noise in MRI has been traditionally modeled as a stationary process governed by a Rician distribution with constant noise power at each vox...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2009
ISSN: 0162-1459,1537-274X
DOI: 10.1198/jasa.2009.0029