An automatic method for estimating noise-induced signal variance in magnitude-reconstructed magnetic resonance images

Lin-Ching Chang; Gustavo Kunde Rohde; Carlo Pierpaoli

doi:10.1117/12.596008

29 April 2005 An automatic method for estimating noise-induced signal variance in magnitude-reconstructed magnetic resonance images

Lin-Ching Chang, Gustavo Kunde Rohde, Carlo Pierpaoli

Author Affiliations +

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.596008
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

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.

Citation Download Citation

Lin-Ching Chang, Gustavo Kunde Rohde, and Carlo Pierpaoli "An automatic method for estimating noise-induced signal variance in magnitude-reconstructed magnetic resonance images", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.596008

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