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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 638568, 12 pages
Research Article

Adaptively Tuned Iterative Low Dose CT Image Denoising

1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada M5S 3G9
2Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, Toronto, ON, Canada M5G 2N2
3Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada M5B 2K3

Received 23 January 2015; Revised 2 May 2015; Accepted 3 May 2015

Academic Editor: Hugo Palmans

Copyright © 2015 SayedMasoud Hashemi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction.