Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means
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.
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Benou et al.'s ensemble model's qualitative and quantitative (MSE) denoising results were better than spatiotemporal Beltrami, moving average, the dynamic Non Local Means method, and stacked denoising autoencoders.
“The qualitative and quantitative (MSE) denoising results were better than spatiotemporal Beltrami, moving average, the dynamic Non Local Means method [182], and stacked denoising autoencoders [183].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [182]
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Application of Noise Invalidation Denoising in MRI
Elahi, P. (2012)
- Authors
- Gal, Y., Mehnert, A. J. H., Bradley, A. P., McMahon, K., Kennedy, D., & Crozier, S.
- Affiliations
- The University of Queensland (AU)
- Greenslopes Private Hospital (AU)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2010-02
- Volume
- 29
- Issue
- 2
- Pages
- 302-310
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2099440229
- Cited by
- 78
- References
- 42
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 1 · 2020: 5 · 2019: 4 · 2018: 5 · 2017: 6 · 2016: 6 · 2015: 12 · 2014: 2 · 2013: 9 · 2012: 7
- FWCI
- 5.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- UWA Profiles and Research Repository (University of Western Australia) (submittedVersion)
- Chalmers Publication Library (Chalmers University of Technology) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Keywords
- dynamic contrast-enhanced MRI, image denoising, non-local means, temporal redundancy, breast MRI, observer evaluation
- MeSH terms
- Algorithms, Breast, Computer Simulation, Contrast Media, Female, Humans, Image Enhancement, Magnetic Resonance Imaging, Normal Distribution
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