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JSDP 2020, 17(3): 101-108 |
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Kazemitabar J, Tavakkoli M. A Bayesian approach for image denoising in MRI. JSDP 2020; 17 (3) :101-108
URL: http://jsdp.rcisp.ac.ir/article-1-893-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-893-en.html
Babol Noshirvani University of Technology
Abstract: (2559 Views)
Magnetic Resonance Imaging (MRI) is a notable medical imaging technique that is based on Nuclear Magnetic Resonance (NMR). MRI is a safe imaging method with high contrast between soft tissues, which made it the most popular imaging technique in clinical applications. MR Image's visual quality plays a vital role in medical diagnostics that can be severely corrupted by existing noise during the acquisition process. Therefore, the denoising of these images has great importance in medical applications. During the last decades, lots of MR denoising approaches from various groups of techniques have been proposed that can be classified into two general groups of acquisition-based noise reduction and post-acquisition denoising methods. The first group's approaches will add imaging time and led to a much time-consuming process. The second group's issues are its complicated mathematical equations required for image denoising, in which stochastic algorithms are usually required to solve these complex equations.
This study aims to find an appropriate statical post-acquisition denoising MR imaging method based on the Bayesian technique. Finding the appropriate prior density function also has great importance since the Bayesian technique's performance is related to its prior density function. In this study, the uniform distribution has been applied as the prior density function. The prior uniform distribution function will reduce the Bayesian algorithm to its simplest possible state and lower computational complexity and time consumption. The proposed method can solve the numerical problems with an adequate timing process without complex algorithms and remove noise in less than 120 seconds on average in all cases. To quantitatively assess image improvement, we used the Structural Similarity Function (SSIM) in MATLAB. The similarity with this function shows an average improvement of more than 0.1 in all images. Considering the results, it can be concluded that combining the uniform distribution function as a prior density function and the Bayesian algorithm can significantly reduce the image's noise without the time and computational cost.
This study aims to find an appropriate statical post-acquisition denoising MR imaging method based on the Bayesian technique. Finding the appropriate prior density function also has great importance since the Bayesian technique's performance is related to its prior density function. In this study, the uniform distribution has been applied as the prior density function. The prior uniform distribution function will reduce the Bayesian algorithm to its simplest possible state and lower computational complexity and time consumption. The proposed method can solve the numerical problems with an adequate timing process without complex algorithms and remove noise in less than 120 seconds on average in all cases. To quantitatively assess image improvement, we used the Structural Similarity Function (SSIM) in MATLAB. The similarity with this function shows an average improvement of more than 0.1 in all images. Considering the results, it can be concluded that combining the uniform distribution function as a prior density function and the Bayesian algorithm can significantly reduce the image's noise without the time and computational cost.
Type of Study: Research |
Subject:
Paper
Received: 2018/08/28 | Accepted: 2020/08/18 | Published: 2020/12/5 | ePublished: 2020/12/5
Received: 2018/08/28 | Accepted: 2020/08/18 | Published: 2020/12/5 | ePublished: 2020/12/5
References
1. [1] Z. Hongtu et al, "Regression models for identifying noise sources in magnetic resonance images," Journal of the American Statistical Association, vol.104, no.486, pp.623-637, 2009. [DOI:10.1198/jasa.2009.0029] [PMID] [PMCID]
2. [2] J. Mohan, V.Krishnaveni, and G.Yanhui , "A survey on the magnetic resonance image denoising methods," Biomedical Signal Processing and Control , vol.9, pp. 56-69, 2014. [DOI:10.1016/j.bspc.2013.10.007]
3. [3] A.Macovski, "Noise in MRI," Magnetic Resonance in Medicin, vol.36,no.3, pp. 494-497, 1996. [DOI:10.1002/mrm.1910360327] [PMID]
4. [4] H. Gudbjartsson, P. Samuel Patz, "The Rician distribution of noisy MRI data," Magnetic resonance in medicine, vol.34, no.6, pp. 910-914, 1995. [DOI:10.1002/mrm.1910340618] [PMID] [PMCID]
5. [5] L. He, R. Ian Greenshields, "A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images," IEEE transactions on medical imaging, vol.28, no.2, pp. 165-172, 2009. [DOI:10.1109/TMI.2008.927338] [PMID]
6. [6] J. Sijbers & et al, "Estimation of the noise in magnitude MR images," Magnetic Resonance Imaging, vol.16, no.1, pp.87-90, 1998. [DOI:10.1016/S0730-725X(97)00199-9]
7. [7] J. Sijbers & et al, "Maximum-likelihood estimation of Rician distribution parameters," IEEE Transactions on Medical Imaging. Vol.17, no.3, pp.357-361, 1998. [DOI:10.1109/42.712125] [PMID]
8. [8] J.Sijbers, A. J. Den Dekker, "Maximum likelihood estimation of signal amplitude and noise variance from MR data," Magnetic Resonance in Medicine, vol.51, no.3, pp.586-594, 2004. [DOI:10.1002/mrm.10728] [PMID]
9. [9] J.Rajan & et al, "Maximum likelihood estimation-based denoising of magnetic resonance images using restricted local neighborhoods," Physics in Medicine & Biology, vol.56, no.16, pp.5221, 2011. [DOI:10.1088/0031-9155/56/16/009] [PMID]
10. [10] J.Rajan & et al, "Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images," Magnetic Resonance Imaging, vol.30, no.10, pp. 1512-1518, 2012. [DOI:10.1016/j.mri.2012.04.021] [PMID]
11. [11] A. Tietze & et al, "Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps," PloS one, vol13, no.9, pp. e0202906, 2018. [DOI:10.1371/journal.pone.0202906] [PMID] [PMCID]
12. [12] L.Lauwers & et al, "Analyzing Rice distributed functional magnetic resonance imaging data: a Bayesian approach," Measurement Science and Technology, vol.21, no.11, pp. 115804, 2010. [DOI:10.1088/0957-0233/21/11/115804]
13. [13] M. Kay, M. Steven, "Fundamentals of statistical signal processing", vol. I: estimation theory, 1993.
14. [14] X. Qi, "Compression of Three-Dimensional Magnetic Resonance Brain Images," 2001.
15. [15] D.Selvathi, and V. Sathananthavathi, "Genetic algorithm based nonlocal maximum likelihood algorithm for MRI denoising," Int. J. Comput. Intell. Telecommun. Syst, vol.2, pp. 21-26, 2011.
16. [16] https://mr.usc.edu/download/data/
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