DOI QR코드

DOI QR Code

Newly-designed adaptive non-blind deconvolution with structural similarity index in single-photon emission computed tomography

  • Kyuseok Kim (Department of Radiological Science, Gachon University) ;
  • Youngjin Lee (Department of Radiological Science, Gachon University)
  • 투고 : 2023.05.07
  • 심사 : 2023.08.28
  • 발행 : 2023.12.25

초록

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

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참고문헌

  1. K. Kim, Y. Lee, Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system, Nucl. Eng. Technol. 53 (2021) 2341-2347, https://doi.org/10.1016/j.net.2021.01.011.
  2. P. Ritt, Recent developments in SPECT/CT, Semin. Nucl. Med. 52 (2022) 276-285, https://doi.org/10.1053/j.semnuclmed.2022.01.004.
  3. L. Imbert, P.-Y. Marie, CZT cameras: a technological jump for myocardial perfusion SPECT, J. Nucl. Cardiol. 23 (2016) 894-896, https://doi.org/10.1007/s12350-015-0216-2.
  4. D. Wu, Z. Zhang, R. Ma, F. Guo, L. Wang, W. Fang, Comparison of CZT SPECT and conventional SPECT for assessment of contractile function, mechanical synchrony and myocardial scar in patients with heart failure, J. Nucl. Cardiol. 26 (2019) 443-452, https://doi.org/10.1007/s12350-017-0952-6.
  5. Y.J. Lee, S.J. Park, S.W. Lee, D.H. Kim, Y.S. Kim, H.J. Kim, Comparison of photon counting and conventional scintillation detectors in a pinhole SPECT system for small animal imaging: Monte Carlo simulation studies, J. Kor. Phys. Soc. 62 (2013) 1317-1322, https://doi.org/10.3938/jkps.62.1317.
  6. J.V. Hoffmann, J.P. Janssen, T. Kanno, T. Shibutani, M. Onoguchi, C. Lapa, J.-P. Grunz, A.K. Buck, T. Higuchi, Performance evaluation of fifth-generation ultrahigh-resolution SPECT system with two stationary detectors and multi-pinhole imaging, EJNMMI. Phys. 7 (2020) 64, https://doi.org/10.1186/s40658-020-00335-6.
  7. T. Yamamoto, J.M. Kim, K.S. Lee, T. Takayama, T. Kitahara, Development of a new cardiac and torso phantom for verifying the accuracy of myocardial perfusion SPECT, Journal of Radiological Science and Technology 31 (2008) 389-400.
  8. M. Lyra, Filtering in SPECT image reconstruction, Int. J. Biomed. Imag. 2011 (2011), 693795, https://doi.org/10.1155/2011/693795.
  9. A.M. Katua, A.O. Ankrah, M. Vorster, A. van Gelder, M.M. Sathekge, Optimization of ordered subset expectation maximization reconstruction for reducing urinary bladder artifacts in single-photon emission computed tomography imaging, World J. Nucl. Med. 10 (2011) 3-8, https://doi.org/10.4103/1450-1147.82108.
  10. O.H. Winz, S. Hellwig, M. Mix, W.A. Weber, F.M. Mottaghy, W.M. Schafer, P. T. Meyer, Image quality and data quantification in dopamine transporter SPECT: advantage of 3-dimensional OSEM reconstruction? Clin. Nucl. Med. 37 (2012) 866-871, https://doi.org/10.1097/RLU.0b013e318251e1b3.
  11. G.L. Zeng, A filtered backprojection algorithm with characteristics of the iterative landweber algorithm, Med. Phys. 39 (2012) 603-607, https://doi.org/10.1118/1.3673956.
  12. K. Van Laere, M. Koole, I. Lemahieu, R. Dierckx, Image filtering in single-photon emission computed tomography: principels and applications, Comput. Med. Imag. Graph. 25 (2001) 127-133, https://doi.org/10.1016/S0895-6111(00)00063-X.
  13. Z. Wang, A.C. Bovik, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13 (2004) 600-612, https://doi.org/10.1109/TIP.2003.819861.
  14. W. Xue, L. Zhang, X. Mou, A.C. Bovik, Gradient magnitude similarity deviation: a highly efficient perceptual image quality index, IEEE Trans. Image Process. 23 (2014) 684-695, https://doi.org/10.1109/TIP.2013.2293423.
  15. J. Dame, A. Chandra, A. Jones, N. Berend, J. Magnussen, G. King, Airway dimensions measured from microcomputed tomography and high-resolution computed tomography, Eur. Respir. J. 28 (2006) 712-720, https://doi.org/10.1183/09031936.06.00012405.
  16. K. Choi, J. Wang, L. Zhu, T.S. Suh, S. Boyd, L. Xing, Compressed sensing based cone-beam computed tomography reconstruction with a first-order method, Med. Phys. 37 (2004) 5113-5125, https://doi.org/10.1118/1.3481510.
  17. M.M.A. Dietze, W. Branderhorst, B. Kunnen, M.A. Viergever, H.W.A.M. de Jong, Accelerated SPECT image reconstruction with FBP and an image enhancement convolutional neural network, EJNMMI. Phys. 6 (2019) 14, https://doi.org/10.1186/s40658-019-0252-0.
  18. A.P. Dempster, N.M. Laird, D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. Ser. B (Methodol.) 39 (1977) 1-38, https://doi.org/10.1111/j.2517-6161.1977.tb01600.x.
  19. L.A. Shepp, Y. Vardi, Maximum likelihood reconstruction for emission tomography, IEEE Trans. Med. Imag. 1 (1982) 113-122, https://doi.org/10.1109/TMI.1982.4307558.
  20. C. Kamphuis, F.J. Beekman, Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm, IEEE Trans. Med. Imag. 17 (1998) 1101-1105, https://doi.org/10.1109/42.746730.
  21. H. Erdogan, J.A. Fessler, Ordered subsets algorithms for transmission tomography, Phys. Med. Biol. 44 (1999) 2835, https://doi.org/10.1088/0031-9155/44/11/311.
  22. F.J. Beekman, C. Kamphuis, Ordered subset reconstruction for x-ray CT, Phys. Med. Biol. 46 (2001) 1835, https://doi.org/10.1088/0031-9155/46/7/307.
  23. T.F. Chan, L.A. Vese, Active contours without edges, IEEE Trans. Image Process. 10 (2001) 266-277, https://doi.org/10.1109/83.902291.
  24. R.T. Whitaker, A level-set approach to 3D reconstruction from range data, Int. J. Comput. Vis. 29 (1998) 203-231, https://doi.org/10.1023/A:1008036829907.
  25. P. Soille, Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, ISBN 978-3-642-07696-1, pp. 173-174.
  26. C. Louchet, L. Mosian, Total variation as a local filter, SIAM J. Imag. Sci. 4 (2011) 651-6940, https://doi.org/10.1137/100785855.
  27. M.-H. Lee, C.-S. Yun, K. Kim, Y. Lee, For the Alzheimer Disease Neuroimaging Initative, Image restoration algorithm incorporating methods to remove noise and blurring from positron emission tomography imaging for Alzheimer's disease diagnosis, Phys. Med. 103 (2022) 181-189, https://doi.org/10.1016/j.ejmp.2022.10.016.
  28. S.H. Chan, R. Khoshabeh, K.B. Gibson, P.E. Gill, T.Q. Nguyen, An augmented Lagrangian method for video restoration, IEEE Trans. Image Process. 20 (2011) 3097-3111, https://doi.org/10.1109/ICASSP.2011.5946560.
  29. J.H. Lee, Y.R. Kim, G.M. Lee, J.H. Ryu, E.Y. Cho, Y.H. Lee, K.-H. Yoon, Coefficient of variation on Gd-EOB MR imaging: correlation with the presence of early-stage hepatocellular carcinoma in patients with chronic hepatitis B, Eur. J. Radiol. 102 (2018) 95-101, https://doi.org/10.1016/j.ejrad.2018.02.032.
  30. M. Koutalonis, H. Delis, G. Spyrou, L. Costaridou, G. Tzanakos, G. Panayiotakis, Contrast-to-noise ratio in magnification mammography: a Monte Carlo study, Phys. Med. Biol. 52 (2007) 3185-3199, https://doi.org/10.1088/0031-9155/52/11/017.
  31. F.H.P. van Velden, R.W. Kloet, B.N.M. van Berckel, S.P.A. Wolfensberger, A. A. Lammertsma, R. Boellaard, Comparison of 3D-OP-OSEM and 3D-FBP reconstruction algorithms for High-Resolution Research Tomograph studies: effects of randoms estimation methods, Phys. Med. Biol. 53 (2008) 3217-3230, https://doi.org/10.1088/0031-9155/53/12/010.
  32. G.A. Kastis, A. Gaitanis, Y. Fernandez, G. Kontaxakis, A.S. Fokas, Evaluation of a spline reconstruction technique: comparison with FBP, MLEM and OSEM, IEEE Nuclear Science Symposuim & Medical Imaging Conference (2011), https://doi.org/10.1109/NSSMIC.2010.5874412.
  33. A. Sowa-Staszczak, W. Lenda-Tracz, M. Tomaszuk, B. Gtowa, A. HubalewskaDydejczyk, Optimization of image reconstruction method for SPECT studies performed using [99mTc-EDDA/HYNIC] octreotate in patients with neuroendocrine tumors, Nucl. Med. Rev. Cent. E Eur. 16 (2013) 9-16, https://doi.org/10.5603/NMR.2012.0002.