Comparison of statistical methods in MR imaging (original) (raw)
Related papers
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).
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.
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).
Parameter estimation from magnitude MR images
International Journal of Imaging Systems and Technology, 1999
This article deals with the estimation of model-based parameters, such as the noise variance and signal components, from magnitude magnetic resonance (MR) images. Special attention has been paid to the estimation of T 1 -and T 2 -relaxation parameters. It is shown that most of the conventional estimation methods, when applied to magnitude MR images, yield biased results. Also, it is shown how the knowledge of the proper probability density function of magnitude MR data (i.e., the Rice distribution) can be exploited so as to avoid (or at least reduce) such systematic errors. The proposed method is based on maximum likelihood (ML) estimation.
Bayesian image processing in magnetic resonance imaging
Magnetic Resonance Imaging, 1991
In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the' processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to he a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior den&y, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.
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.
Restoration Algorithm for Gaussian Corrupted MRI Using Non-local Averaging
Advances in Intelligent Systems and Computing, 2015
Magnetic Resonance Images (MRI) are known to be corrupted by the additive Gaussian noise during the acquisition process. The presence of this noise affects the diagnosis as it tends to alter image details and pixel intensities. Conventional iterative denoising approaches fail to preserve the details and structures during MRI restoration. This paper proposes a Non-Local Averaging based MRI denoising algorithm to facilitate preservation of the finer structures. The proposed algorithm computes the weighted average of the similar pixels of the image within the local window. Method noise has been used as a measure for detail preservation which corresponds to the difference between original and the restored image. Simulation trials are performed on the image at differing levels of Gaussian noise which are then justified by method noise analysis and performance evaluation factors such as Peak Signal-Noise Ratio (PSNR) and Structural Similarity (SSIM). The proposed algorithm has demonstrated good performance, both in terms of visual quality as well as values of performance parameter.
Proceedings of SPIE, 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.
2001 EMBC_Improved MRI Rec_Bayesian_No 1393_D.pdf
The goal of this paper is to present the development of a new reconstruction methodology for restoring Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of Neural Network models and Bayesian restoration, in the problem of MRI image extraction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to make measurement time smaller by reducing scanning trajectories as much as possible. In this way, however, underdetermined equations are introduced and poor image reconstruction follows. It is suggested here that significant improvements could be achieved, concerning quality of the extracted image, by judiciously applying Neural Network and Bayesian estimation methods to the k -space data. More specifically, it is demonstrated that Neural Network techniques could construct efficient priors and introduce them in the procedure of Bayesian reconstruction. These ANN Priors are independent of specific image properties and probability distributions. They are based on training supervised Multilayer Perceptron (MLP) neural filters to estimate the missing samples of complex k -space and thus, to improve k -space information capacity. Such a neural filter based prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the traditional Bayesian MRI reconstruction approach as well as by the pure MLP based reconstruction approach.
Medical Physics, 2010
The region-of-interest ͑ROI͒ selection and evaluation is one of the key factors in the successful evaluation of radiological images. However, the presence of noise in images may lead to incorrect diagnosis. The aim of this study was to test the hypothesis that the weighting by error estimation in ROI assessment might significantly improve the validity of the results. Methods: As a model, the data maps of the transverse relaxation time constants ͑T 2 ͒ from patients who underwent a matrix-associated chondrocyte transplantation procedure on the femoral condyle were analyzed. Artificial noise with a Rician density probability distribution was added to each TE image. ROIs were processed either as a regular arithmetic mean or as a weighted mean, in which weighted coefficients were calculated with regard to fitting error estimates ͓coefficient of determination ͑R 2 ͒; root mean squared error ͑RMSE͒, mean absolute error ͑MSE͒, mean squared error ͑MAE͒, and chi-squared error ͑ 2 ͔͒. Results: The global T 2 values in repair tissue ͑meanϮ standard deviation, 62Ϯ 7 ms; range 51-70 ms͒ and in healthy cartilage ͑meanϮ SD, 49Ϯ 6 ms; range 40-60 ms͒ were significantly different ͑p Ͻ 0.001͒. With a 45% or greater decrease from the original SNR value ͑corresponding to a noise level of 35% of random value͒, the statistical significance was lost ͑P Ͼ 0.05͒; however, the use of the coefficient of determination ͑R 2 ͒ as a correction factor was able to maintain the p-value of Ͻ0.05 up to a 56% decrease from the original SNR value. Conclusions: The results of this study can prospectively be applied in a wide range of radiological imaging techniques in cases when error estimation is possible. Our analysis on MR images with artificially added noise showed that utilization of the correlation of determination ͑R 2 ͒ as a weighting parameter in ROI evaluation may significantly improve the differentiation between native and transplanted cartilage tissue in noisy images. This could be an added benefit in the non-invasive monitoring of the post-operative status of patients with cartilage transplants if the MR images are not ideal ͑e.g., lower field strength or lower SNR͒.