Abstract
In this study we propose a novel correction scheme that filters Magnetic Resonance Images data, by using a modified Linear Minimum Mean Square Error (LMMSE) estimator which takes into account the joint information of the local features. A closed-form analytical solution for our estimator is presented and it proves to make the filtering process far simpler and faster than other estimation techniques that rely on iterative optimization scheme and require multiple data samples. An experimental validation of our correction scheme was carried out through large scale experiments using both clinical and synthetic MR images, artificially corrupted with rician noise of σ varying from 1 to 40. These noisy images were filtered using our proposed method against the classical LMMSE, the Non-Local Means filter and the Nonlocality-Reinforced Convolutional Neural Networks (NRCNN) techniques. The results show an outstanding performance of our proposed method, given the fact that from σ ≈ 12 onwards, the proposed method outperforms all other methods. Another attention-grabbing feature of our method is that its Structural Similarity does not vary sharply [0.87, 0.95] across the σ spectrum as the other three techniques, which implies that this method can work on a wider range of deteriorated images than the rest of the techniques.
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References
Aja-Fernandez S., Alberola-Lopez C., Westin C. F.: Noise and signal estimation in magnitude MRI and rician distributed images: A LMMSE approach. IEEE Trans. Image Process. 17: 1383–1398, 2008
Baselice F., Ferraioli G., Pascazio V.: A 3D MRI denoising algorithm based on Bayesian theory. BioMedical Eng. 16: 1–19, 2017
Buades A., Coll B., Morel J. M.: A non-local algorithm for image denoising.. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol 2. IEEE, 2005, pp 60–65
Chen G., Wu Y., Shen D., Yap P. T. (2018) Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space. Med. Phys., 1–36
Chen G., Wu Y., Shen D., Yap P. T.: Noise reduction in diffusion mri using non-local self-similar information in joint x- q space. Med. Image Anal. 53: 79–94, 2019
Christa M. S., Nan-Kuei C.: Improving the accuracy, quality, and signal-to-noise ratio of MRI parametric mapping using Rician bias correction and parametric-contrast-matched principal component analysis (PCM-PCA). Yale J. Biol. Med. 91: 207–214, 2018
Cristovão C., Foi A., Katkovnik V., Egiazarian K.: Nonlocality-reinforced convolutional neural networks for image denoising. IEEE Signal Process. Lett. 25: 1216–1220, 2018
Cruz C., Foi A., Katkovnik V., Egiazarian K.: Nonlocality-reinforced convolutional neural networks for image denoising. IEEE Signal Process. Lett. 25 (8): 1216–1220, 2018
Filho A. S., Garrido C. E., dos Santos A. C., Murta L. O.: Enhancing quality in diffusion tensor imaging with anisotropic anomalous diffusion filter. Res. Biomed. Eng. 33: 247–258, 2017
Gao G.: Characterization of SAR Clutter and its Applications to Land and Ocean Singapore: Springer, 2019, p 166
Henkelman R. M.: Measurement of signal intensities in the presence of noise in MR images. Med. Phys. 12: 232–233, 1985
Hongli L., Renfag W. (2019) Denoising 3D Magnetic Resonance Images based on low-rank tensor approximation with adaptive multirank estimation. IEEE Access
https://brainweb.bic.mni.mcgill.ca/brainweb/: Access: 15th july 2020. web (2020)
Hu K., Cheng Q., Li B., Gao X.: The complex data denoising in MR images based on the directional extension for the undecimated wavelet transform. Biomed. Signal Process. Control 39: 336–350, 2018
Idier J., Collewet G. (2014) Properties of Fisher information for Rician distributions and consequences in MRI
Idier J., Collewet G. (2015) Properties of fisher information for rician distributions and consequences in MRI. HAL archives-ouvertes, pp. 1–17
Jiang Q., Moussaoui S., Idier J., Collewet G., Xu M. (2017) Majorization-minimization algorithms for maximum likelihood estimation of Magnetic Resonance Images. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6
Krishnamoorthy K. (2016) Handbook of statistical distributions with applications. CRC Press
Marques J. P., Simonis F., Webb A. (2019) Low-field mri: An mr physics perspective: Low-field MRI. J. Magn. Reson. Imaging, 49
Ndajah P., Kikuchi H., Yukawa M., Watanabe H., Muramatsu S.: An investigation on the quality of denoised images. Int. J. Circuits, Syst. Signal Process. 5 (4): 423–434, 2011
Reducindo I., Arce-Santana E., Campos D., Alba A.: Multimodal image registration by particle filtering: Evaluation and new results. IEEE Lat. Am. Trans. 12: 129–137, 2014
Rice S. O.: Mathematical analysis of random noise, reprinted by wax n. ”Selected papers on noise and stochastic processes”. Bell Syst. Tech. J. 23 (24): 1954, 1944
Abhishek S., Chaurasia V.: A review on magnetic resonance images denoising techniques. Mach. Intell. Signal Anal. 748: 707–715, 2019
Sijbers J., Dekker A. J.: Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magn. Reson. Med. 51: 586–594, 2004
Sijbers J., Poot D., den Dekker A. J., Pintjenst W.: Automatic estimation of the noise variance from the histogram of a magnetic resonance image. Phys. Med. Biol. 52: 1335–1348, 2007
Sijbers J., Rajan J., Veraart J., Van Audekerke J.: Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images. Magn. Reson. Imaging 30: 1512–1518, 2012
Sijbers J., Veraart J., Dmitry S. N., Christiaens D., Adesaron B., Fieremans E. (2016) Denoising of diffusion MRI using random matrix theory. Neuroimage, 394–406
Tristan-Vega A., García-Perez V., Aja-Fernandez S., Westin C. F.: Efficient and robust nonlocal means denoising of MR data based on salient features matching. Comput. Methods Programs Biomed. 105: 131–144, 2012
Wang Z., Bovik A., Sheikh H. R., Simoncelli E.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process 13: 600–612, 2014
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The authors would like to extend their gratitude to the Instituto Politécnico Nacional of México and the CONACyT of México, for their support to this research work – project 240820.
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Kinani, J.M.V., Silva, A.R., Mújica-Vargas, D. et al. Rician Denoising Based on Correlated Local Features LMMSE Approach. J Med Syst 45, 40 (2021). https://doi.org/10.1007/s10916-020-01696-2
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DOI: https://doi.org/10.1007/s10916-020-01696-2