Kalman Filter for Artifact Reduction in MRI Imaging: A Literature Review

Abstract:

Background: The appearance of Noise Artifacts is admittedly very disturbing the quality of MRI diagnostic images. The application of BLADE and STIR sequences based on artificial intelligence technology has described in reference that it is able to suppress moving signals from the vascular and signals from fat tissue. However, the long consumption of scanning time is one of the drawbacks that arise, and subsequently affect the presence of noise in the image. The use of another technique, namely kallman filter with the Matlab (Matrix Laboratory) program, which is applied as part of post-scanning image processing will help reduce image noise values ​​that arise due to the problem of long scanning time consumption in both sequences. The aim of this literature review is to determine the potential of the filter kalman for the reduction of artifacts on MRI examinations. Methods: The search was conducted using google scholar, WILEY, IEE Explore, SPRINGER, Scopus and PERPUSNAS in English with the article period 2004-2020 using the keywords MRI artifacts, reducing artifacts and the Kalman filter algorithm. Result: The results of a review of 4 articles of kalman filter intervention on MRI Brain, MRI Abdomen and MR Cardiac showed that kalman filter was good enough in reducing artifacts and increasing anatomical information. Conclusion: Kalman filter has the potential to reduce artifacts, improve image quality and clarify anatomical images on MRI examinations.

• Periodical: Applied Mechanics and Materials (Volume 913)
• Pages: 79-88
• DOI: https://doi.org/10.4028/p-143r36
• Online since: March 2023
• Authors: Dyah Ayu Puspitaningtyas*, Donny Kristanto Mulyantoro, Sudiyono Sudiyono
• Keywords: Health Services, Innovation, Kalman filter, MRI Artifacts, Technological Capabilities

References

[1] K. Somasundaram and P. Kalavathi, Analysis of Imaging Artifacts in MR Brain Images,, Orient. J. Comput. Sci. …, vol. 5 N0 1, p.135–141, 2012, [Online]. Available: http://computerscijournal.org/dnload/Somasundaram-K-and-Kalavathi-P-/OJCSV05I01P135-141.pdf.

[2] N. Mahmood, A. Shah, A. Waqas, A. Abubakar, S. Kamran, and S. B. Zaidi, Image segmentation methods and edge detection: An application to knee joint articular cartilage edge detection,, J. Theor. Appl. Inf. Technol., vol. 71, no. 1, p.87–96, (2015).

[3] M. Zaitsev, J. Maclaren, and M. Herbst, Motion artifacts in MRI: A complex problem with many partial solutions,, J. Magn. Reson. Imaging, vol. 42, no. 4, p.887–901, 2015,.

DOI: 10.1002/jmri.24850

[4] R. L. Ehman and J. P. Felmlee, Flow artifact reduction in MRI: A review of the roles of gradient moment nulling and spatial presaturation,, Magn. Reson. Med., vol. 14, no. 2, p.293–307, 1990,.

DOI: 10.1002/mrm.1910140214

[5] J. Zhuo and R. P. Gullapalli, MR Artifacts, Safety, and Quality Control,, RadioGraphics, vol. 26, no. 1, p.275–297, 2006,.

DOI: 10.1148/rg.261055134

[6] R. Kijowski and G. E. Gold, Routine 3D magnetic resonance imaging of joints,, J. Magn. Reson. Imaging, vol. 33, no. 4, p.758–771, 2011,.

DOI: 10.1002/jmri.22342

[7] R. B. Frobell, H. P. Roos, E. M. Roos, F. W. Roemer, J. Ranstam, and L. S. Lohmander, Treatment for acute anterior cruciate ligament tear: Five year outcome of randomised trial,, BMJ, vol. 346, no. 7895, p.1–12, 2013,.

DOI: 10.1136/bmj.f232

[8] R. Golfieri, H. Baddeley, J. S. Pringle, and R. Souhami, The role of the STIR sequence in magnetic resonance imaging examination of bone tumours,, Br. J. Radiol., vol. 63, no. 748, p.251–256, 1990,.

DOI: 10.1259/0007-1285-63-748-251

[9] M. J. Sampson, M. P. Jackson, C. J. Moran, R. Moran, S. J. Eustace, and S. Shine, Three Tesla MRI for the diagnosis of meniscal and anterior cruciate ligament pathology: a comparison to arthroscopic findings,, Clin. Radiol., vol. 63, no. 10, p.1106–1111, 2008,.

DOI: 10.1016/j.crad.2008.04.008

[10] S. Särkka and A. Solin, Filtering of Physiological Noise: DRIFTER,, vol. 60, no. 2, p.1517–1527, 2012,.

[11] K. S.Tamilselvan, G. Murugesan, and M. Vinothsaravanan, A Histogram based Hybrid Approach for Medical Image Denoising using Wavelet and Curvelet Transforms,, Int. J. Comput. Appl., vol. 74, no. 21, p.6–11, 2013,.

DOI: 10.5120/13040-0053

[12] M. Athans, Kalman filtering,, Control Syst. Handb. Control Syst. Adv. Methods, Second Ed., no. June, p.295–306, 2010,.

[13] X. Feng, M. Salerno, C. M. Kramer, and C. H. Meyer, Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging,, Magn. Reson. Med., vol. 69, no. 5, p.1346–1356, 2013,.

DOI: 10.1002/mrm.24375

[14] D. W. Kim, C. Kim, D. H. Kim, and D. H. Lim, Rician nonlocal means denoising for MR images using nonparametric principal component analysis,, EURASIP J. Image Video Process., vol. 2011, no. 1, p.1–8, 2011,.

DOI: 10.1186/1687-5281-2011-15

[15] J. Shin, S. Ahn, and X. Hu, Correction for the T1 effect incorporating flip angle estimated by Kalman filter in cardiac-gated functional MRI,, Magn. Reson. Med., vol. 70, no. 6, p.1626–1633, 2013,.

DOI: 10.1002/mrm.24620

[16] S. Sharifzadeh Javidi and H. Saligheh Rad, Using Kalman Filter to Improve the Accuracy of Diffusion Coefficients in MR Imaging: A Simulation Study,, Iran. J. Radiol., vol. 16, no. Special Issue, p.1–2, 2019,.

DOI: 10.5812/iranjradiol.99153

[17] E. A. Moore, M. J. Graves, and M. R. Prince, From Picture to Proton,, (1997).

[18] M. Gupta, H. Taneja, L. Chand, and V. Goyal, Enhancement and analysis in MRI image denoising for different filtering techniques,, J. Stat. Manag. Syst., vol. 21, no. 4, p.561–568, 2018,.

DOI: 10.1080/09720510.2018.1466964

[19] Z. Q. Abdullah, Quality assessment on medical image denoising algorithm: diffusion and wavelet transform filters,, no. April, (2014).

[20] F. Baselice, G. Ferraioli, and V. Pascazio, A 3D MRI denoising algorithm based on Bayesian theory,, Biomed. Eng. Online, vol. 16, no. 1, p.1–19, 2017,.

DOI: 10.1186/s12938-017-0319-x

[21] D. Hernando et al., Removal of olefinic fat chemical shift artifact in diffusion MRI,, Magn. Reson. Med., vol. 65, no. 3, p.692–701, 2011,.

DOI: 10.1002/mrm.22670

[22] E. M. Bellon, E. M. Haacke, P. E. Coleman, D. A. Steiger, and E. Gangarosa, MR Artifacts :,, (1986).

[23] Y. Wang, Y. Song, H. Xie, W. Li, B. Hu, and G. Yang, Reduction of Gibbs artifacts in magnetic resonance imaging based on Convolutional Neural Network,, Proc. - 2017 10th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2017, vol. 2018-January, no. October, p.1–5, 2018,.

DOI: 10.1109/cisp-bmei.2017.8302197

[24] C. Zhao et al., HHS Public Access,, p.132–141, 2020,.

[25] F. Shuaib, D. Kimbrough, M. Roofe, G. McGwin Jr, and P. Jolly, 基因的改变NIH Public Access,, Bone, vol. 23, no. 1, p.1–7, 2008,.

[26] D. A. Steinman and B. K. Rutt, On the nature and reduction of plaque-mimicking flow artifacts in black blood MRI of the carotid bifurcation,, Magn. Reson. Med., vol. 39, no. 4, p.635–641, 1998,.

DOI: 10.1002/mrm.1910390417

[27] J. Olsrud, J. Lätt, S. Brockstedt, B. Romner, and I. M. Björkman-Burtscher, Magnetic resonance imaging artifacts caused by aneurysm clips and shunt valves: Dependence on field strength (1.5 and 3 T) and imaging parameters,, J. Magn. Reson. Imaging, vol. 22, no. 3, p.433–437, 2005,.

DOI: 10.1002/jmri.20391

[28] Y. Hirokawa, H. Isoda, Y. S. Maetani, S. Arizono, K. Shimada, and K. Togashi, MRI artifact reduction and quality improvement in the upper abdomen with PROPELLER and Prospective Acquisition Correction (PACE) technique,, Am. J. Roentgenol., vol. 191, no. 4, p.1154–1158, 2008,.

DOI: 10.2214/ajr.07.3657

[29] D. Tamada, M. L. Kromrey, S. Ichikawa, H. Onishi, and U. Motosugi, Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced mr imaging of the liver,, Magn. Reson. Med. Sci., vol. 19, no. 1, p.64–76, 2020,.

DOI: 10.2463/mrms.mp.2018-0156

[30] T. B. Smith and K. S. Nayak, MRI artifacts and correction strategies,, Imaging Med., vol. 2, no. 4, p.445–457, 2010,.

[31] A. Yoshikawa et al., Heart Rate and Respiration Affect the Functional Connectivity of Default Mode Network in Resting-State Functional Magnetic Resonance Imaging,, Front. Neurosci., vol. 14, no. June, p.1–13, 2020,.

DOI: 10.3389/fnins.2020.00631

[32] B. F. Lane, F. Q. Vandermeer, R. C. Oz, E. W. Irwin, A. B. McMillan, and J. J. Wong-You-Cheong, Comparison of sagittal T2-weighted BLADE and fast spin-echo MRI of the female pelvis for motion artifact and lesion detection,, Am. J. Roentgenol., vol. 197, no. 2, p.307–313, 2011,.

DOI: 10.2214/ajr.10.5918

[33] K. Krupa and M. Bekiesińska-Figatowska, Artifacts in magnetic resonance imaging,, Polish J. Radiol., vol. 80, no. 1, p.93–106, 2015,.

DOI: 10.12659/pjr.892628

[34] Y. Zhao et al., Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques,, Proc. Int. Jt. Conf. Neural Networks, vol. 2021-July, 2021,.

DOI: 10.1109/ijcnn52387.2021.9534191

[35] B. Chen, L. Zhang, H. Chen, K. Liang, and X. Chen, A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors,, Comput. Methods Programs Biomed., vol. 200, p.105797, 2021,.

DOI: 10.1016/j.cmpb.2020.105797

[36] R. M. Chen, S. C. Yang, and C. M. Wang, MRI brain tissue classification using unsupervised optimized extenics-based methods,, Comput. Electr. Eng., vol. 58, p.489–501, 2017,.

DOI: 10.1016/j.compeleceng.2017.01.018

[37] P. Spincemaille, T. D. Nguyen, M. R. Prince, and Y. Wang, Kalman filtering for real-time navigator processing,, Magn. Reson. Med., vol. 60, no. 1, p.158–168, 2008,.

DOI: 10.1002/mrm.21649

[38] S. C. Yang et al., Contrast enhancement and tissues classification of breast MRI using Kalman filter-based linear mixing method,, Comput. Med. Imaging Graph., vol. 33, no. 3, p.187–196, 2009,.

DOI: 10.1016/j.compmedimag.2008.12.001

[39] C. M. Wang, G. C. Lin, C. Y. Lin, and R. M. Chen, An unsupervised Kalman filter-based linear mixing approach to MRI classification,, IEEE Asia-Pacific Conf. Circuits Syst. Proceedings, APCCAS, vol. 2, p.1105–1108, 2004,.

DOI: 10.1109/apccas.2004.1413077

[40] X. Li, Y. H. Lee, S. Mikaiel, J. Simonelli, T. C. Tsao, and H. H. Wu, Respiratory motion prediction using fusion-based multi-rate kalman filtering and real-time golden-angle radial mri,, IEEE Trans. Biomed. Eng., vol. 67, no. 6, p.1727–1738, 2020,.

DOI: 10.1109/tbme.2019.2944803