Segmentation Based Noise Variance Estimation from Background MRI Data

Rajan, Jeny; Poot, Dirk H. J.; Juntu, Jaber; Sijbers, Jan

doi:10.1007/978-3-642-13772-3_7

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…This was found to be the case for all fitting methods and for all three signal models. Clearly, the presence of nonrandom artifacts such as streaking or ghosting requires more sophisticated estimates of noise . In any event, in actual application, the strategy of independent noise estimation is to be preferred over incorporation of yet another parameter into the Bayesian or NLLS analysis.…”

Section: Discussionmentioning

confidence: 99%

Bayesian analysis of transverse signal decay with application to human brain

Bouhrara

Reiter

Spencer

2014

Magnetic Resonance in Med

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Purpose: Transverse relaxation analysis with several signal models has been used extensively to determine tissue and material properties. However, the derivation of corresponding parameter values is notoriously unreliable. We evaluate improvements in the quality of parameter estimation using Bayesian analysis and incorporating Rician noise, as appropriate for magnitude MR images. Theory and Methods: Monoexponential, stretched exponential, and biexponential signal models were analyzed using nonlinear least squares (NLLS) and Bayesian approaches. Simulations, and phantom and human brain data, were analyzed using three different approaches to account for noise. Parameter estimation bias, reflecting accuracy, and dispersion, reflecting precision, were derived for a range of signal-to-noise ratios (SNR) and relaxation parameters. Results: All methods performed well at high SNR. At lower SNR, the Bayesian approach yielded parameter estimates of considerably greater precision, as well as greater accuracy, than did NLLS. Incorporation of Rician noise greatly improved accuracy and, to a somewhat lesser extent, precision, in derived transverse relaxation parameters. Analyses of data obtained from solution phantoms and from brain were consistent with simulations. Conclusion: Overall, estimation of parameters characterizing several different transverse relaxation models was markedly improved through use of Bayesian analysis and through incorporation of Rician noise.

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Section: Discussionmentioning

confidence: 99%

Bayesian analysis of transverse signal decay with application to human brain

Bouhrara

Reiter

Spencer

2014

Magnetic Resonance in Med

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“…random variables with distribution function F X and E X 4 < ∞. Let TX n (s) be defined as in (7), then…”

Section: Asymptoticsmentioning

confidence: 99%

“…Ref. [7] improved this estimation with the use of background segmentation, by fitting the density function of the Rayleigh distribution to the histogram of the segmented background in order to estimate the noise variance. The estimation of the noise forms a crucial part in efficiently denoising the MRI as well as in the quality assessment of these images.…”

Section: Introductionmentioning

confidence: 99%

Testing for the Rayleigh Distribution: A New Test with Comparisons to Tests for Exponentiality Based on Transformed Data

2022

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We propose a new goodness-of-fit test for the Rayleigh distribution which is based on a distributional fixed-point property of the Stein characterization. The limiting null distribution of the test is derived and the consistency against fixed alternatives is also shown. The results of a finite-sample comparison is presented, where we compare the power performance of the new test to a variety of other tests. In addition to existing tests for the Rayleigh distribution we also exploit the link between the exponential and Rayleigh distributions. This allows us to include some powerful tests developed specifically for the exponential distribution in the comparison. It is found that the new test outperforms competing tests for many of the alternative distributions. Interestingly, the highest estimated power, against all alternative distributions considered, is obtained by one of the tests specifically developed for the Rayleigh distribution and not by any of the exponentiality tests based on the transformed data. The use of the new test is illustrated on a real-world COVID-19 data set.

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“…In a small volume, for instance, the presence of partial volume pixels in its boundary could strongly influence the SNR estimation. Hence, a general planar region delineation may not be precise even when applying more complex algorithms such as in Rajan et al [2010], Tsiotsios and Petrou [2013].…”

Section: Planar Region Delineationmentioning

confidence: 99%

Anisotropic Diffusion Filtering Operation and Limitations - Magnetic Resonance Imaging Evaluation

Palma¹,

Cappabianco²,

Ide

et al. 2014

IFAC Proceedings Volumes

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High-frequency noise is present in magnetic resonance images and it is usually removed by a filtering process. The anisotropic diffusion filter (ADF) was proposed to adaptively remove the noise, maintaining the image edges. However, as quantified in this paper for the first time, ADF methods still produce unsatisfactory results. While previous ADF implementations used the gradient of the strongest edges or the standard deviation of a planar region (i.e. without any edges), it is proposed a novel approach with improved parameter estimation based on both edge and planar region, overcoming some of ADF important limitations. Our framework was validated in more than thirty magnetic resonance images leading to more consistent results.

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Segmentation Based Noise Variance Estimation from Background MRI Data

Cited by 7 publications

References 7 publications

Bayesian analysis of transverse signal decay with application to human brain

Bayesian analysis of transverse signal decay with application to human brain

Testing for the Rayleigh Distribution: A New Test with Comparisons to Tests for Exponentiality Based on Transformed Data

Anisotropic Diffusion Filtering Operation and Limitations - Magnetic Resonance Imaging Evaluation

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