DOI QR코드

DOI QR Code

Noise Level Evaluation According to Slice Thickness Change in Magnetic Resonance T2 Weighted Image of Multiple Sclerosis Disease

다발성 경화증 질환의 자기공명 T2 강조영상에서 단면 두께 변화에 따른 잡음 평가

  • Hong, Inki (Department of Radiological Science, Gachon University) ;
  • Park, Minji (Department of Radiological Science, Gachon University) ;
  • Kang, Seong-Hyeon (Department of Radiological Science, Gachon University) ;
  • Lee, Youngjin (Department of Radiological Science, Gachon University)
  • 홍인기 (가천대학교 방사선학과) ;
  • 박민지 (가천대학교 방사선학과) ;
  • 강성현 (가천대학교 방사선학과) ;
  • 이영진 (가천대학교 방사선학과)
  • Received : 2021.07.22
  • Accepted : 2021.08.12
  • Published : 2021.08.31

Abstract

Magnetic resonance imaging(MRI) uses strong magnetic field to image the cross-section of human body and has excellent image quality with no risk of radiation exposure. Because of above-mentioned advantages, MRI has been widely used in clinical fields. However, the noise generated in MRI degrades the quality of medical images and has a negative effect on quick and accurate diagnosis. In particular, examining a object with a detailed structure such as brain, image quality degradation becomes a problem for diagnosis. Therefore, in this study, we acquired T2 weighted 3D data of multiple sclerosis disease using BrainWeb simulation program, and used quantitative evaluation factors to find appropriate slice thickness among 1, 3, 5, and 7 mm. Coefficient of variation and contrast to noise ratio were calculated to evaluate the noise level, and root mean square error and peak signal to noise ratio were used to evaluate the similarity with the reference image. As a result, the noise level decreased as the slice thickness increased, while the similarity decreased after 5 mm. In conclusion, as the slice thickness increases, the noise is reduced and the image quality is improved. However, since the edge signal is lost due to overlapped signal, it is considered that selecting appropriate slice thickness is necessary.

Keywords

References

  1. Zhou J, Payen JF, Wilson DA, Traystman RJ, van Zijl PC. Using the amide proton signals of intra-cellular proteins and peptides to detect pH effects in MRI. Nature Medicine. 2003;9(8):1085-90. https://doi.org/10.1038/nm907
  2. Sharma A, Hamarneh G. Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Transactions on Medical Imaging. 2019;39(4):1170-83. https://doi.org/10.1109/tmi.2019.2945521
  3. Antoch G, Bockisch A. Combined PET/MRI: A new dimension in whole-body oncology imaging? European Journal of Nuclear Medicine and Molecular Imaging. 2009;36(1):113-20. https://doi.org/10.1007/s00259-008-0951-6
  4. Min JW, Jeong HW, Han JH, Lee SN, Han SY, Kim KW, et al. Study on the resolution characteristics by using magnetic resonance imaging 3.0T. Journal of Radiological Science and Technology. 2020;43(4):251-7. https://doi.org/10.17946/JRST.2020.43.4.251
  5. Macovski A. Noise in MRI. Magnetic Resonance in Medicine. 1996;36(3):494-7. https://doi.org/10.1002/mrm.1910360327
  6. Kim HG, Choi S. The impact of signal intensity and image distortion magnetic resonance imaging in the orthopedic prosthetic metal. Journal of Radiological Science and Technology. 2012;35(4):321-6.
  7. Honal M, Leupold J, Huff S, Baumann T, Ludwig U. Compensation of breathing motion artifacts for MRI with continuously moving table. Magnetic Resonance in Medicine. 2010;63(3):701-12. https://doi.org/10.1002/mrm.22162
  8. Savio SJ, Harrison LC, Luukkaala T, Heinonen T, Dastidar P, Soimakallio S, et al. Effect of slice thickness on brain magnetic resonance image texture analysis. Biomedical Engineering Online. 2010;9(1):1-14. https://doi.org/10.1186/1475-925X-9-1
  9. Smith TB. MRI artifacts and correction strategies. Imaging in Medicine. 2010;2(4):445. https://doi.org/10.2217/iim.10.33
  10. Butts K, Pauly JM, Gold GE. Reduction of blurring in view angle tilting MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2005;53(2):418-24. https://doi.org/10.1002/mrm.20375
  11. Mahmoudzadeh AP, Kashou NH. Interpoltation-based super-resolution reconstruction: Effects of slice thickness. Journal of Medical Imaging. 2014 December;1(3):034007.
  12. Schmierer K, Wheeler-Kingshott CA, Boulby PA, Scaravilli F, Altmann DR, Barker GJ, et al. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage. 2007;35(2):467-77. https://doi.org/10.1016/j.neuroimage.2006.12.010
  13. Turner B, Lin X, Calmon G, Roberts N, Blumhardt LD. Cerebral atrophy and disability in relapsing-remitting and secondary progressive multiple sclerosis over four years. Multiple Sclerosis Journal. 2003;9(1):21-7. https://doi.org/10.1191/1352458503ms868oa
  14. Cocosco CA, Kollokian V, Kwan RKS, Pike GB, Evans AC. Brainweb: Online interface to a 3D MRI simulated brain database. NeuroImage. 1997;5(4).
  15. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, et al. Design and construction of a realistic digital brain phantom. IEEE Transactions on Medical Imaging. 1998;17(3):463-8. https://doi.org/10.1109/42.712135
  16. Kim NY, Kim JH, Lim J, Kang SH, Lee YJ. Evaluation of tendency for characteristics of MRI train T2 weighted images according to changing NEX: MRiLab simulation study. Journal of the Korean Society of Radiology. 2021 February;15(1):9-14. https://doi.org/10.7742/JKSR.2021.15.1.9
  17. Sudeep PV, Palanisamy P, Kesavadas C, Rajan J. Nonlocal linear minimum mean square error methods for denoising MRI. Biomedical Signal Processing and Control. 2015 July;20:125-34. https://doi.org/10.1016/j.bspc.2015.04.015
  18. Isa IS, Sulaiman SN, Mustapha M, Darus S. Evaluating denoising performances of fundamental filters for T2-weighted MRI images. Procedia Computer Science. 2015;60:760-8. https://doi.org/10.1016/j.procs.2015.08.231
  19. Kwan RKS, Evans AC, Pike GB. MRI simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging. 1999;18(11):1085-97. https://doi.org/10.1109/42.816072
  20. Kwan RKS, Evans AC, Pike GB. An extensible MRI simulator for post-processing evaluation. In: Hohne KH, Kikinis R. Visualization in Biomedical Computing. Lecture Notes in Computer Science. 1996;1131:135-40.
  21. Gopalan K, Tamir JI, Arias AC, Lustig M. Quantitative anatomy mimicking slice phantoms. Magnetic Resonance in Medicine. 2021 March; 86(2):1159-66. https://doi.org/10.1002/mrm.28740
  22. He Y, Cao S, Zhang H, Sun H, Wang F, Zhu H, et al. Dynamic PET image denoising with deep learning-based joint filtering. IEEE Access. 2021;9:41998-2012. https://doi.org/10.1109/ACCESS.2021.3064926
  23. Kasiri K, Javad DM, Kazemi K, Sadegh HM, Kafshgari S. Comparison evaluation of three brain MRI segmentation methods in software tools. 2010 17th Iranian Conference of Biomedical Engineering (ICBME). 2010 November;1-4.
  24. Nakamura K, Fisher E. Segmentation of brain magnetic resonance images for measurements of gray matter atrophy in multiple sclerosis patients. NeuroImage. 2009 February;44(3):769-76. https://doi.org/10.1016/j.neuroimage.2008.09.059
  25. Dolezal O, Dwyer MG, Horakova D, Havrodva D, Minagar A, Balachandran S, et al. Detection of cortical lesions is dependent on choice of slice thickness in patients with multiple sclerosis. International Review of Neurobiology. 2007;79:475-89. https://doi.org/10.1016/S0074-7742(07)79021-9
  26. Rousseau F, Faisan S, Heitz F, Armspach JP, Chevalier Y, Blanc F, et al. An a contrario approcah for change detection in 3D multimodal images: Application to multiple sclerosis in MRI. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2007 August;2069-72.
  27. Lee SY, Cho JH, Lee HK, Cho MS, Park CS, Kim EC, et al. A study on a method to reduce the effect of the cross-talk artifact in a simultaneous, multiple-slice, plane, oblique MRI scan. Journal of the Korean Physical Society. 2012 September; 61:807-14. https://doi.org/10.3938/jkps.61.807
  28. Lee YJ, Han CH, Lee JC, Kim ST, Oh GY, Cho SW, et al. MR imaging with LAIR pulse sequence in various cerebral lesions: comparison with T2-weighted imaging. Journal of Korean Radiological Society. 1998;38:397-401. https://doi.org/10.3348/jkrs.1998.38.3.397
  29. Absinta M, Sati P, Fechner A, Schindler MK, Nair G, Reich DS. Identification of chronic active multiple sclerosis lesions on 3T MRI. American Journal of Neuroradiology. 2018 July;39(7):1233-8. https://doi.org/10.3174/ajnr.A5660
  30. Wang KY, Uribe TA, Lincoln CM. Comparing lesion detection of infratentorial multiple sclerosis lesions between T2-weighted spin-echo, 2D-FLAIR, and 3D-FLAIR sequences. Clinical Imaging. 2018 September-October;51:229-34. https://doi.org/10.1016/j.clinimag.2018.05.017
  31. Gabr RE, Lincoln JA, Kamali A, Arevalo O, Zhang X, Sun X, et al. Sensitive detection of infratentorial and upper cervical cord lesions in multiple sclerosis with combined 3D FLAIR and T2-weighted (FLAIR3) imaging. American Journal of Neuroradiology. 2020 November;41(11):2062-7. https://doi.org/10.3174/ajnr.a6797