Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Investigation of a blind-deconvolution framework after noise reduction using a gamma camera in nuclear medicine imaging

  • Kim, Kyuseok (Department of Radiation Convergence Engineering, Yonsei University) ;
  • Lee, Min-Hee (Department of Pediatrics, Wayne State University School of Medicine) ;
  • Lee, Youngjin (Department of Radiological Science, Gachon University)
  • Received : 2019.12.12
  • Accepted : 2020.04.28
  • Published : 2020.11.25
Download PDF
⟨ Previous Next ⟩

Abstract

A gamma camera system using radionuclide has a functional imaging technique and is frequently used in the field of nuclear medicine. In the gamma camera, it is extremely important to improve the image quality to ensure accurate detection of diseases. In this study, we designed a blind-deconvolution framework after a noise-reduction algorithm based on a non-local mean, which has been shown to outperform conventional methodologies with regard to the gamma camera system. For this purpose, we performed a simulation using the Monte Carlo method and conducted an experiment. The image performance was evaluated by visual assessment and according to the intensity profile, and a quantitative evaluation using a normalized noise-power spectrum was performed on the acquired image and the blind-deconvolution image after noise reduction. The result indicates an improvement in image performance for gamma camera images when our proposed algorithm is used.

Keywords

References

  1. H.O. Anger, Scintillation camera with multichannel collimators, J. Nucl. Med. 5 (1964) 515-531.
  2. T.E. Peterson, L.R. Furenlid, SPECT detectors: the Anger camera and beyond, Phys. Med. Biol. 56 (2011) R145-R182. https://doi.org/10.1088/0031-9155/56/17/R01
  3. R.J. Shearer, A.R. Constable, M. Girling, W.F. Hendry, J.D. Fergusson, Radioisotopic bone scintigraphy with the gamma camera in the investigation of prostatic cancer, Br. Med. J. 2 (1974) 362-365. https://doi.org/10.1136/bmj.2.5915.362
  4. K. Kimura, K. Hashikawa, H. Etani, A. Uehara, T. Kozuka, H. Moriwaki, Y. Isaka, M. Matsumoto, T. Kamada, H. Moriyama, H. Tabuchi, A new apparatus for brain imaging: four-head rotating gamma camera single-photon emission computed tomograph, J. Nucl. Med. 31 (1990) 603-609.
  5. G.U. Hung, Y.F. Wang, H.Y. Su, T.C. Hsieh, C.L. Ko, R.F. Yen, New trends in radionuclide myocardial perfusion imaging, Acta Cardiol. Sin. 32 (2016) 156-166.
  6. J.D. Sain, H.H. Barrett, Performance evaluation of a modular gamma camera using a detectability index, J. Nucl. Med. 44 (2003) 58-66.
  7. C.V. Cannistraci, A. Abbas, X. Gao, Median modified Wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra, Sci. Rep. 5 (2017), https://doi.org/10.1038/srep08017.
  8. N.H. Mahmood, M.R.M. Razif, M.T.A.N. Gany, Comparison between median, unsharp and Wiener filter and its effect on ultrasound stomach tissue image segmentation for pyloric stenosis, Int. J. Appl. Sci. Technol. 1 (2011) 218-226.
  9. P. Patidar, M. Gupta, S. Srivastava, A.K. Nagawat, Image de-noising by various filters for different noise, Int. J. Comput. Appl. 9 (2010) 45-50.
  10. K. Seo, S.H. Kim, S.H. Kang, J. Park, C.L. Lee, Y. Lee, The effect of total variation (TV) technique for noise reduction in radio-magnetic X-ray image: quantitative study, J. Magnetics 21 (2016) 593-598. https://doi.org/10.4283/JMAG.2016.21.4.593
  11. C.R. Park, Y. Lee, Fast non-local means noise reduction algorithm with acceleration function for improvement of image quality in gamma camera system: a phantom study, Nucl. Eng. Technol. 51 (2019) 719-722. https://doi.org/10.1016/j.net.2018.12.013
  12. H.V. Bhujle, B.H. Vadavadagi, NLM based magnetic resonance image denoising - a review, Biomed. Signal Process Contr. 47 (2019) 252-261. https://doi.org/10.1016/j.bspc.2018.08.031
  13. X. Wang, H. Wang, J. Yang, Y. Zhang, A new method for nonlocal means image denoising using multiple images, PloS One 11 (2016), https://doi.org/10.1371/journal.pone.0158664.
  14. S. Lee, H. Cho, Y. Lee, High-energy industrial 2D X-ray imaging system with effective nonlocal means denoising for nondestructive testing, Nucl. Instrum. Methods Phys. Res. A 925 (2019) 212-216. https://doi.org/10.1016/j.nima.2019.01.060
  15. J. Shim, M. Yoon, Y. Lee, Quantitative study of fast non-local means-based denoising filter in chest X-ray imaging with lung nodule using threedimensional printing, Optik 179 (2019) 1180-1188. https://doi.org/10.1016/j.ijleo.2018.10.118
  16. Z.A. Ameen, G. Sulong, A novel Zohair filter for deblurring computed tomography medical images, Int. J. Imag. Syst. Technol. 25 (2015) 265-275. https://doi.org/10.1002/ima.22143
  17. R. Gonzalez, R. Woods, Digital Image Processing, third ed., Pearson Education, New Jersey, 2009.
  18. J.L. Starck, F. Murtagh, A. Bijaoui, Image Processing and Data Analysis, Cambridge University Press, Cambridge, 1998.
  19. M. Makitalo, A. Foi, Optimal inversion of the Anscombe transformation in low-count Poisson image denoising, IEEE Trans. Image Process. 20 (2010) 99-109. https://doi.org/10.1109/TIP.2010.2056693
  20. A. Buades, B. Coll, J. Morel, A review of image denoising algorithms, with a new one, Multiscale Model. Simul. 4 (2006) 490-530. https://doi.org/10.1137/040616024
  21. M. Makital, A. Foi, A closed-form approximation of the exact unbiased inverse of the anscombe variance-stabilizing transformation, IEEE Trans. Image Process. 20 (2011) 2697-2698. https://doi.org/10.1109/TIP.2011.2121085
  22. D. Geman, C. Yang, Nonlinear image recovery with half-quadratic regularization, IEEE Trans. Image Process. 4 (1995) 932-946. https://doi.org/10.1109/83.392335
  23. J. Pan, Z. Su, Fast l0-regularized kernel estimation for rubust motion deblurring, IEEE Trans. Signal Processing Letters 20 (2013) 841-844. https://doi.org/10.1109/LSP.2013.2261986
  24. J. Anger, M. Delbracio, G. Facciolo, Effective Blind Deblurring under High Noise Levels, 2019 arXiv:1904.09154.
  25. D. Sudipto, O. Michailovich, Blind deconvolution of medical ultrasound images using variable splitting and proximal point methods, in: 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, 2011, https://doi.org/10.1109/ISBI.2011.5872341. Chicago, IL, USA, 09 June.
  26. S. Chen, X. Wang, O. Elgendy, Plug-and-play ADMM for image restoration: fixed-point convergence and applications, IEEE Trans Comput Imag 3 (2016) 84-98. https://doi.org/10.1109/TCI.2016.2629286
  27. J. Dobbins III, E. Samei, N. Ranger, Y. Chen, Intercomparison of methods for image quality characterization. II. Noise power spectrum, Med. Phys. 33 (2006) 1466-1475. https://doi.org/10.1118/1.2188816

Cited by

  1. Comparison of noise estimation methods used in denoising99mTc-sestamibi parathyroid images using wavelet transform vol.20, pp.1, 2020, https://doi.org/10.4103/wjnm.wjnm_43_20
  2. Blind Deconvolution Based on Compressed Sensing with bi-l0-l2-norm Regularization in Light Microscopy Image vol.18, pp.4, 2020, https://doi.org/10.3390/ijerph18041789
  3. Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system vol.53, pp.7, 2021, https://doi.org/10.1016/j.net.2021.01.011