Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Block-matching and 3D filtering algorithm in X-ray image with photon counting detector using the improved K-edge subtraction method

  • Kyuseok Kim (Department of Biomedical Engineering, Eulji University) ;
  • Youngjin Lee (Department of Radiological Science, Gachon University)
  • Received : 2023.07.17
  • Accepted : 2024.01.09
  • Published : 2024.06.25
Download PDF
⟨ Previous Next ⟩

Abstract

Among photon counting detector (PCD)-based technologies, the K-edge subtraction (KES) method has a very high material decomposition efficiency. Yet, since the increase in noise in the X-ray image to which the KES method is applied is inevitable, research on image quality improvement is essential. Here, we modeled a block-matching and 3D filtering (BM3D) algorithm and applied it to PCD-based X-ray images with the improved KES (IKES) method. For PCD modeling, Monte Carlo simulation was used, and a phantom composed of iodine substances with different concentrations was designed. The IKES method was modeled by adding a log term to KES, and the X-ray image used for subtraction was obtained by applying the 3.0 keV range based on the K-edge region of iodine. As a result, the IKES image using the BM3D algorithm showed the lowest normalized noise power spectrum value. In addition, we confirmed that the contrast-to-noise ratio and no-reference-based evaluation results when the BM3D algorithm was applied to the IKES image were improved by 29.36 % and 20.56 %, respectively, compared to the noisy image. In conclusion, we demonstrated that the IKES imaging technique using a PCD-based detector and the BM3D algorithm fusion technique were very efficient for X-ray imaging.

Keywords

References

  1. J. Liu, Y. Xu, A. Teymurazyan, Z. Papandreou, G. Pang, Development of a novel high quantum efficiency MV x-ray detector for image-guided radiotherapy: a feasibility study, Med. Phys. 47 (2020) 152-162. https://doi.org/10.1002/mp.13900
  2. M.A.A. Hegazy, M.E. Eldib, Y.J. Mun, M.H. Cho, M.H. Cho, S.Y. Lee, A bench-top micro-CT capable of simulating head motions, Biomed. Eng. Lett. 7 (2017) 237-244. https://doi.org/10.1007/s13534-017-0023-6
  3. P.M. Johns, J.C. Nino, Room temperature semiconductor detectors for nuclear security, J. Appl. Phys. 126 (2019) 040902.
  4. A. Esquivel, A. Ferrero, A. Mileto, F. Baffour, K. Horst, P.S. Rajiah, A. Inoue, S. Leng, C. McCollough, J.G. Fletcher, Photon-counting detector CT: key points radiologists should know, Korean J. Radiol. 23 (2022) 854-865. https://doi.org/10.3348/kjr.2022.0377
  5. S. Leng, M. Bruesewitz, S. Tao, K. Rajendran, A.F. Halaweish, N.G. Compeau, J. G. Fletcher, C.H. McCollough, Photon-counting detector CT: system design and clinical applications of an emerging technology, Radiographics 39 (2019) 729-743. https://doi.org/10.1148/rg.2019180115
  6. T. Flohr, M. Petersilka, A. Henning, S. Ulzheimer, J. Ferda, B. Schmidt, Photoncounting CT review, Phys. Med. 79 (2020) 126-136. https://doi.org/10.1016/j.ejmp.2020.10.030
  7. R. Ballabriga, J. Alozy, F.N. Bandi, M. Campbell, N. Egidos, J.M. Fernandez- Tenllado, E.H.M. Heijne, I. Kremastiotis, X. Llopart, B.J. Madsen, D. Pennicard, V. Sriskaran, L. Tlustos, Photon counting detectors for X-ray imaging with emphasis on CT, IEEE Trans. Radiat. Plasma Med. Sci. 5 (2021) 422-440. https://doi.org/10.1109/TRPMS.2020.3002949
  8. 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
  9. G. Daniel, Y. Gutierrez, O. Limousin, Application of a deep learning algorithm to Compton imaging of radioactive point sources with a single planar CdTe pixelated detector, Nucl. Eng. Technol. 54 (2022) 1747-1753. https://doi.org/10.1016/j.net.2021.10.031
  10. Y. Chen, X. Wang, Q. Song, J. Xu, B. Mu, Development of a high-energy-resolution EDXRD system with a CdTe detector for security inspection, AIP Adv. 8 (2018) 105113.
  11. D. Graafen, T. Emrich, M.C. Halfmann, P. Mildenberger, C. Duber, Y. Yang, A. E. Othman, J. O'Doherty, L. Muller, R. Kloeckner, Dose reduction and image quality in photon-counting detector high-resolution computed tomography of the chest, J. Thorac. Imag. 37 (2022) 315-322.
  12. E. Roessl, R. Proksa, K-edge imaging in x-ray computed tomography using multibin photon counting detectors, Phys. Med. Biol. 52 (2007) 4679-4696. https://doi.org/10.1088/0031-9155/52/15/020
  13. P. He, B. Wei, W. Cong, G. Wang, Optimization of K-edge imaging with spectral CT, Med. Phys. 39 (2012) 6572-6579. https://doi.org/10.1118/1.4754587
  14. E. Roessl, B. Brendel, K.J. Engel, J.P. Schlomka, A. Thran, R. Proksa, Sensitivity of photon-counting based K-edge imaging in X-ray computed tomography, IEEE Trans. Med. Imag. 30 (2011) 1678-1690. https://doi.org/10.1109/TMI.2011.2142188
  15. V.D. Trapani, P. Oliva, F. Arfelli, L. Brombal, R.H. Menk, P. Delogu, Development and validation of a simulation tool for K-edge Subtraction imaging with polychromatic spectra and X-ray photon counting detectors, Nucl. Instrum. Methods Phys. Res., Sect. A 1046 (2023) 167675.
  16. R. Granger, L. Salvo, S.R. du Roscoat, P. Lhuissier, Optimal parameters estimation for K-edge subtraction imaging using PixiRad-2/PixieIII photon counting detector on a conventional laboratory X-ray micro-tomograph, J. Instrum. 18 (2023), https://doi.org/10.1088/1748-0221/18/02/P02006.
  17. Y.N. Choi, S. Lee, H.J. Kim, Reducing radiation dose by application of optimized low-energy x-ray filters to K-edge imaging with a photon counting detector, Phys. Med. Biol. 61 (2016) N35-N49. https://doi.org/10.1088/0031-9155/61/2/N35
  18. S.W. Lee, Y.N. Choi, H.M. Cho, Y.J. Lee, H.J. Ryu, H.J. Kim, Feasibility of photoncounting K-edge imaging in X-ray and computed tomographic systems: Monte Carlo simulation studies, J. Kor. Phys. Soc. 59 (2011) 2833-2839. https://doi.org/10.3938/jkps.59.2833
  19. Y. Lee, A.C. Lee, H.J. Kim, A Monte Carlo simulation study of an improved K-edge log-subtraction X-ray imaging using a photon counting CdTe detector, Nucl. Instrum. Methods Phys. Res., Sect. A 830 (2016) 381-390. https://doi.org/10.1016/j.nima.2016.06.024
  20. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising with blockmatching and 3D filtering, Image Process.: Algorithms Syst., Neural Networks., Mach. Learn. 6064 (2006), https://doi.org/10.1117/12.643267.
  21. T. Zhao, J. Hoffman, M. McNitt-Gray, D. Ruan, Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics, Med. Phys. 46 (2019) 190-198. https://doi.org/10.1002/mp.13252
  22. T. Kirti, K. Jitendra, S. Ashok, Poisson noise reduction from X-ray images by region classification and response median filtering, Sadhana 42 (2017) 855-863. https://doi.org/10.1007/s12046-017-0654-4
  23. Y.J. Lee, D.H. Kim, H.J. Kim, The effect of high-resolution parallel-hole collimator materials with a pixelated semiconductor SPECT system at equivalent sensitivities: Monte Carlo simulation studies, J. Kor. Phys. Soc. 64 (2014) 1055-1062. https://doi.org/10.3938/jkps.64.1055
  24. L.A. Lehmann, R.E. Alvarez, A. Macovski, W.R. Brody, N.J. Pelc, S.J. Riederer, A. L. Hall, Generlized image combinations in dual KVP digital radiography, Med. Phys. 8 (1981) 659-667. https://doi.org/10.1118/1.595025
  25. K. Kim, Y. Lee, Effects of total variation regularization noise reduction algorithm in improved K-edge log-subtraction X-ray images with photoncounting cadmium telluride detectors, Optik 206 (2020) 164380.
  26. M. Lebrun, An analysis and implementation of the BM3D image denoising method, Image Process. Line 2 (2012) 175-213. https://doi.org/10.5201/ipol.2012.l-bm3d
  27. K. Davov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. 16 (2007) 2080-2095. https://doi.org/10.1109/TIP.2007.901238
  28. K. Kim, Y. Lee, A systematic review of trends for image quality improvement in light microscopy, J. Radiol. Sci. Technol. 46 (2023) 207-217.
  29. S.W. Kim, W.K. Choi, S. Kim, Evaluation of the effects of differences in metal artifact type and location on image quality in computed tomography scans, J. Med. Phys. 48 (2023) 80-84.
  30. A. Mittal, A.K. Moorthy, A.C. Bovik, No-reference image quality assessment in the spatial domain, IEEE Trans. Image Process. 21 (2012) 4695-4708. https://doi.org/10.1109/TIP.2012.2214050
  31. A. Kumar, Nonlocal means image denoising using orthogonal moments, Appl. Opt. 54 (2015) 8156-8165. https://doi.org/10.1364/AO.54.008156
  32. Y. Do, Y. Cho, S.H. Kang, Y. Lee, Optimization of block-matching and 3D filtering (BM3D) algorithm in brain SPECT imaging using fan beam collimator: phantom study, Nucl. Eng. Technol. 54 (2022) 3403-3414. https://doi.org/10.1016/j.net.2022.04.008
  33. D.J. Crotty, R.L. McKinley, M.P. Tornai, Experimental spectral measurements of heavy K-edge filtered beams for x-ray computed mammotomography, Phys. Med. Biol. 52 (2007) 603-616. https://doi.org/10.1088/0031-9155/52/3/005