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**Harvard**

Gal, Y., Mehnert, A., Bradley, A., McMahon, K., Kennedy, D. och Crozier, S. (2010) *Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means*.

** BibTeX **

@article{

Gal2010,

author={Gal, Y. and Mehnert, Andrew and Bradley, A. P. and McMahon, K. and Kennedy, D. and Crozier, S.},

title={Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means},

journal={IEEE Transactions on Medical Imaging},

issn={0278-0062},

volume={29},

issue={2},

pages={302-310},

abstract={This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.},

year={2010},

keywords={denoising, dynamic contrast-enhanced (dce) magnetic resonance imaging (mri), dynamic nonlocal means (dnlm), noise, nonlocal means, magnetic-resonance images, anisotropic diffusion, noise removal, rician noise, filter, perfusion, breast, robust, space },

}

** RefWorks **

RT Journal Article

SR Print

ID 156520

A1 Gal, Y.

A1 Mehnert, Andrew

A1 Bradley, A. P.

A1 McMahon, K.

A1 Kennedy, D.

A1 Crozier, S.

T1 Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means

YR 2010

JF IEEE Transactions on Medical Imaging

SN 0278-0062

VO 29

IS 2

SP 302

OP 310

AB This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.

LA eng

OL 30