Parameter estimation from magnitude MR images (original) (raw)
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Estimation of the Noise in Magnitude MR Images
Magnetic Resonance Imaging, 1998
Magnitude Magnetic Resonance (MR) data are Rician distributed. In this note a new method is proposed to estimate the image noise variance for this type of data distribution. The method is based on a double image acquisition, thereby exploiting the knowledge of the Rice distribution moments.
Maximum likelihood estimation of signal amplitude and noise variance from MR data
Magnetic Resonance in Medicine, 2004
In magnetic resonance imaging, the raw data, which are acquired in spatial frequency space, are intrinsically complex valued and corrupted by Gaussian distributed noise. After applying an inverse Fourier transform the data remain complex valued and Gaussian distributed. If the signal amplitude is to be estimated, one has two options. It can be estimated directly from the complex valued data set, or one can first perform a magnitude operation on this data set, which changes the distribution of the data from Gaussian to Rician, and estimate the signal amplitude from the thus obtained magnitude image. Similarly, the noise variance can be estimated from both the complex and magnitude data sets.
Robust estimation of the noise variance from background MR data [6144-232]
In the literature, many methods are available for estimation of the variance of the noise in magnetic resonance (MR) images. A commonly used method, based on the maximum of the background mode of the histogram, is revisited and a new, robust, and easy to use method is presented based on maximum likelihood (ML) estimation. Both methods are evaluated in terms of accuracy and precision using simulated MR data. It is shown that the newly proposed method outperforms the commonly used method in terms of mean-squared error (MSE).
Robust estimation of the noise variance from background MR data
2006
In the literature, many methods are available for estimation of the variance of the noise in magnetic resonance (MR) images. A commonly used method, based on the maximum of the background mode of the histogram, is revisited and a new, robust, and easy to use method is presented based on maximum likelihood (ML) estimation. Both methods are evaluated in terms of accuracy and precision using simulated MR data. It is shown that the newly proposed method outperforms the commonly used method in terms of mean-squared error (MSE).
Noise measurement from magnitude MRI using local estimates of variance and skewness
Physics in Medicine and Biology, 2010
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. (Some figures in this article are in colour only in the electronic version) Sijbers J and Dekker A J den 2004 Maximum likelihood estimation of signal amplitude and noise variance from MR data Magn. Reson. Med. 51 586-94 Sijbers J et al 1998 Maximum likelihood estimation of Rician distribution parameters IEEE Trans. Med. Imag. 17 357-61 Sijbers J et al 2007 Automatic estimation of the noise variance from the histogram of a magnetic resonance image Phys. Med. Biol. 52 1335-48 Zhang Y et al 2001 Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm IEEE Trans. Med. Imaging 20 45-57
Medical Imaging 2005: Image Processing, 2005
Signal intensity in magnetic resonance images (MRIs) is affected by random noise. Assessing noise-induced signal variance is important for controlling image quality. Knowledge of signal variance is required for correctly computing the chi-square value, a measure of goodness of fit, when fitting signal data to estimate quantitative parameters such as T1 and T2 relaxation times or diffusion tensor elements. Signal variance can be estimated from measurements of the noise variance in an object-and ghost-free region of the image background. However, identifying a large homogeneous region automatically is problematic. In this paper, a novel, fully automated approach for estimating the noise-induced signal variance in magnitude-reconstructed MRIs is proposed. This approach is based on the histogram analysis of the image signal intensity, explicitly by extracting the peak of the underlining Rayleigh distribution that would characterize the distribution of the background noise. The peak is extracted using a nonparametric univariate density estimation like the Parzen window density estimation; the corresponding peak position is shown here to be the expected signal variance in the object. The proposed method does not depend on prior foreground segmentation, and only one image with a small amount of background is required when the signal-to-noise ratio (SNR) is greater than three. This method is applicable to magnitude-reconstructed MRIs, though diffusion tensor (DT)-MRI is used here to demonstrate the approach.
Optimal estimation of T2 maps from magnitude MR images
1998
A Maximum Likelihood estimation technique is proposed for optimal estimation of Magnetic Resonance (MR) T 2 maps from a set of magnitude MR images. Thereby, full use is made of the actual probability density function of the magnitude data, which is the Rician distribution. While equal in terms of precision, the proposed method is demonstrated to be superior in terms of accuracy compared to conventional relaxation parameter estimation techniques.
Automatic estimation of the noise variance from the histogram of a magnetic resonance image
Physics in Medicine and Biology, 2007
Estimation of the noise variance of a magnetic resonance (MR) image is important for various post-processing tasks. In the literature, various methods for noise variance estimation from MR images are available, most of them however requiring user interaction and/or multiple (perfectly aligned) images. In this paper, we focus on automatic histogram-based noise variance estimation techniques. Previously described methods are reviewed and a new method based on the maximum likelihood (ML) principle is presented. Using Monte Carlo simulation experiments as well as experimental MR data sets, the noise variance estimation methods are compared in terms of the root mean-squared error (RMSE). The results show that the newly proposed method is superior in terms of the RMSE. † Jan Sijbers is a Postdoctoral
Comparison of statistical methods in MR imaging
International Journal of Imaging Systems and Technology, 1991
The problem of recovering the true underlying scene from a noisy image is considered. Several methods are compared empirically by applying them to magnetic resonance (MR) images. It turns out that a simple method, the Gaussian window filter, gives good results. This method requires only "instantaneous" processing.