Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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Efficiency of Median Modified Wiener Filter Algorithm for Noise Reduction in PET/MR Images: A Phantom Study

PET/MR 영상에서의 팬텀을 활용한 노이즈 감소를 위한 변형된 중간값 위너필터의 적용 효율성 연구

  • Cho, Young Hyun (Department of Radiological Scienece, Jeonju University) ;
  • Lee, Se Jeong (Department of Radiological Scienece, Jeonju University) ;
  • Lee, Youngjin (Department of Radiological Scienece, Gachon University) ;
  • Park, Chan Rok (Department of Radiological Scienece, Jeonju University)
  • 조영현 (전주대학교 방사선학과) ;
  • 이세정 (전주대학교 방사선학과) ;
  • 이영진 (가천대학교 방사선학과) ;
  • 박찬록 (전주대학교 방사선학과)
  • Received : 2021.06.17
  • Accepted : 2021.06.28
  • Published : 2021.06.30
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Abstract

The digital image such as medical X-ray and nuclear medicine field mainly contains noise distribution. The noise degree in image degrades image quality. That is why, the noise reduction algorithm is efficient for medical image field. In this study, we confirmed effectiveness of application for median modified Wiener filter (MMWF) algorithm for noise reduction in PET/MR image compared with median filter image, which is used as conventional noise redcution algorithm. The Jaszczak PET phantom was used by using 18F solution and filled with NaCl+NiSO4 fluids. In addition, the radioactivity ratio between background and six spheres in the phantom is maintained to 1:8. In order to mimic noise distribution in the image, we applied Gaussian noise using MATLAB software. To evlauate image quality, the contrast to noise ratio (CNR) and coefficient of variation (COV) were used. According to the results, compared with noise image and images with MMWF algorithm, the image with MMWF algorithm is increased approximately 33.2% for CNR result, decreased approximately 79.3% for COV result. In conclusion, we proved usefulness of MMWF algorithm in the PET/MR images.

Keywords

References

  1. Townsend DW. Multimodality imaging of structure and function. Physics in Medicine and Biology. 2008;53(4):R1-9. https://doi.org/10.1088/0031-9155/53/4/R01
  2. Boellaard R, Rausch I, Beyer T, Delso G, Yaqub M, Quick HH, et al. Qauality control for quantitative multicenter whole-body PET/MR studies: A NEMA image quality phantom study with three current PET/MR systems. Medical Physics. 2015;42:5961-9. https://doi.org/10.1118/1.4930962
  3. Grant AM, Deller TW, Khalighi MM, Maramraju SH, Delso G, Levin CS. NEMA NU 2-2012 performance studies for the SiPM-basd ToF PET component of the GE SIGNA PET/MR system. Medical Physics. 2016;43:2334-43. https://doi.org/10.1118/1.4945416
  4. Teuho J, Johansson J, Linden J, Saunavaara V, Teras M. Comparison of single-scatter simulation and Monte Carlo single-scatter simulation on Philips Ingenuity TF PET/MR. IEEE symposium on Nuclear Science (NSS/MIC).
  5. Zaidi H, Ojha N, Morich M, Griesmer J, Hu Z, Maniawski P, et al. Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Physics in Medicine and Biology. 2011;56:3091-106. https://doi.org/10.1088/0031-9155/56/10/013
  6. Delso G, Ziegler S. PET/MRI system design. European Journal of Nuclear Medicine and Molecular Imaging. 2009;36:S86-92.
  7. Hofmann M, Pichler B, SchSlkopf B, Beyer T. Towards quantitative PET/MRI: A review of MR-based attenuation correction techniques. European Journal of Nuclear Medicine and Molecular Imaging. 2009; 36:S93-104.
  8. Sharma A, Singh J. Image denoising using spatial domain filters: A quantitative study. IEEE 6th International Congress on Image and Signal Processing. 2013;1:293-8.
  9. Seo KH, Kim SH, Kang SH, Park JW, Lee CL, Lee YJ. The effects of total variation (TV) technique for noise reduction in Radio-Magnetic X-ray image: Quantitative Study. Journal of Magnetics. 2016; 21(4):593-8. https://doi.org/10.4283/JMAG.2016.21.4.593
  10. Lee MS, Kang MG. 3D Non-local Means(NLM) algorithm based on stochastic distance for low-dose X-ray fluoroscopy denoising. Journal of The Institute of Electronics and Information Engineers. 2017;54(4):61-7. https://doi.org/10.5573/ieie.2017.54.4.61
  11. Goldstein JS, Reed IS, Scharf LL. A multistage representation of the wiener filter based on orthogonal projections. IEEE Transactions on Information Theory. 1998;44(7):2943-59. https://doi.org/10.1109/18.737524
  12. Borsdorf A, Raupach R, Flohr T, Hornegger J. Wavelet based noise reduction in CT-images using correlation analysis. IEEE Transactions on Medical Imaging. 2008;27(12):1685-703. https://doi.org/10.1109/TMI.2008.923983
  13. Kalender WA, Klotz E, Kostaridou L. An algorithm for noise suppression in dual energy CT material density images. IEEE Transactions on Medical Imaging. 1988;7(3):218-24. https://doi.org/10.1109/42.7785
  14. Ju SU, An BH, Kang SH, Lee Y. Median Modified Wiener Filter for Noise Reduction in Computed Tomographic Image using Simulated Male Adult Human Phantom. Journal of the Korean Society of Radiology. 2021;15(1):21-8. https://doi.org/10.7742/JKSR.2021.15.1.21
  15. Park CR, Kang SH, Lee Y. Optimization of mask size with median modified Wiener filter algorithm for gamma images using pixelated semiconductor detecor: Monte Carlo simulation study. Nuclear Instruments and Methods in Physics Research A. 2020;980:1-6.
  16. Lee YK, Ahn SM. Usefulness of brain phantom made by fused filament fabrication type 3D printer. Journal of Radiological Science and Technology. 2020:43(6);453-60. https://doi.org/10.17946/JRST.2020.43.6.453
  17. Jeong EH, Oh JY, Lee JY, Park HH. Deep learing application of gamma camera quality control in nuclear medicin. Journal of Radiological Science and Technology. 2020:43(6):461-7. https://doi.org/10.17946/JRST.2020.43.6.461