A new denoising method for dynamic contrast-enhanced MRI

Gal, Yaniv, Mehnert, Andrew, Bradley, Andrew, McMahon, Kerry, Kennedy, Dominic and Crozier, Stuart (2008). A new denoising method for dynamic contrast-enhanced MRI. In: Engineering in Medicine and Biology Society 2008 (EMBS 2008). 30th Annual International IEEE EMBS Conference, Vancouver, B.C.,Canada, (847-850). 20-24 August 2008. doi:10.1109/IEMBS.2008.4649286

Author Gal, Yaniv
Mehnert, Andrew
Bradley, Andrew
McMahon, Kerry
Kennedy, Dominic
Crozier, Stuart
Title of paper A new denoising method for dynamic contrast-enhanced MRI
Conference name 30th Annual International IEEE EMBS Conference
Conference location Vancouver, B.C.,Canada
Conference dates 20-24 August 2008
Proceedings title Engineering in Medicine and Biology Society 2008 (EMBS 2008)   Check publisher's open access policy
Journal name 2008 30th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Vols 1-8   Check publisher's open access policy
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE - Institute of Electrical Electronics Engineers Inc.
Publication Year 2008
Year available 2008
Sub-type Fully published paper
DOI 10.1109/IEMBS.2008.4649286
Open Access Status Not yet assessed
ISBN 9781424418145
ISSN 1557-170X
Start page 847
End page 850
Total pages 4
Language eng
Abstract/Summary This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. The algorithm is called Dynamic Non-Local Means and is a novel variation on the Non-Local Means (NL-Means) algorithm. It exploits the redundancy of information in the DCE-MRI sequence of images. An evaluation of the performance of the algorithm relative to six other denoising algorithms—Gaussian filtering, the original NL-Means algorithm, bilateral filtering, anisotropic diffusion filtering, the wavelets adaptive multiscale products threshold method, and the traditional wavelet thresholding method—is also presented. The evaluation was performed by two groups of expert observers—18 signal/image processing experts, and 9 clinicians (8 radiographers and 1 radiologist)—using real DCE-MRI data. The results of the evaluation provide evidence, at the α=0.05 level of significance, that both groups of observers deem the DNLM algorithm to perform visually better than all of the other algorithms.
Subjects E1
920102 Cancer and Related Disorders
090302 Biomechanical Engineering
0903 Biomedical Engineering
080106 Image Processing
Keyword Algorithms
Contrast media
Image enhancement
Image interpretation
Computer assisted
Magnetic resonance imaging
Reproducibility of results
Sensitivity and specificity
Q-Index Code E1
Q-Index Status Confirmed Code
Grant ID SR0567196
Institutional Status UQ

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