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A permissible region extraction based on a knowledge priori for X-ray luminescence computed tomography

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Abstract

X-ray luminescence computed tomography (XLCT) is a promising imaging technology for biological applications. The reconstruction, however, suffers from severe ill-posedness due to the strong scattering of photon propagation in biological tissues. A permissible region (PR) extraction based on a knowledge priori is proposed to alleviate the ill-posedness in this paper. N groups of recovered result with N groups of different discretized mesh have provided N groups of PR for XLCT, which can be considered as a knowledge priori. The intersection of N groups of PR provides a reasonable PR of nanophosphor. With the PR, an improved recovered result can be obtained. Numerical simulation experiments and physical phantom experiments on a cylinder have demonstrated the feasibility and effectiveness of this strategy.

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References

  1. Ahmad, M., Pratx, G., Bazalova, M., Xing, L.: X-ray luminescence and x-ray fluorescence computed tomography: new molecular imaging modalities. IEEE Access. 2, 1051–1061 (2014)

    Article  Google Scholar 

  2. Liu, X., Liao, Q., Wang, H.: Fast X-ray luminescence computed tomography imaging. IEEE Trans Biomed Eng. 61(6), 1621–1627 (2014)

    Article  Google Scholar 

  3. Chen, D., Zhu, S., Cao, X., Zhao, F., Liang, J.: X-ray luminescence computed tomography imaging based on X-ray distribution model and adaptively split Bregman method. Biomed Opt Express. 6(7), 2649–2663 (2015)

    Article  Google Scholar 

  4. Liu, X., Zhang, B., Luo, J., Bai, J.: 4-D reconstruction for dynamic fluorescence diffuse optical tomography. IEEE Trans Med Imaging. 31(11), 2120–2132 (2012)

    Article  Google Scholar 

  5. Mohajerani, P., Ntziachristos, V.: An inversion scheme for hybrid fluorescence ‎molecular tomography using a fuzzy inference system. IEEE Trans. Med. Imaging. 35(2), 381–390 (2016)

    Article  Google Scholar 

  6. Wang, G., Cong, W., Durairaj, K., Qian, X., Shen, H., Sinn, P., Hoffman, E., McLennan, G., Henry, M.: In vivo mouse studies with bioluminescence tomography. Opt. Express. 14(17), 7801–7809 (2006)

    Article  Google Scholar 

  7. He, X.W., Liang, J.M., Wang, X.R., et al.: Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method. Opt. Express. 18(24), 24825–24841 (2010)

    Article  Google Scholar 

  8. Wang, C., Volotskova, O., Lu, K., Ahmad, M., Sun, C., Xing, L., Lin, W.: Synergistic assembly of heavy metal clusters and luminescent organic bridging ligands in metal–organic frameworks for highly efficient X-ray scintillation. J. Am. Chem. Soc. 136(17), 6171–6174 (2014)

    Article  Google Scholar 

  9. Osakada, Y., Pratx, G., Sun, C., Sakamoto, M., Ahmad, M., Volotskova, O., Ong, Q., Teranishi, T., Harada, Y., Xing, L., Cui, B.: Hard X-ray-induced optical luminescence via biomolecule-directed metal clusters. Chem. Commun. 50(27), 3549–3551 (2014)

    Article  Google Scholar 

  10. Pratx, G., Carpenter, C.M., Sun, C., Rao, R.P., Xing, L.: Tomographic molecular imaging of X-ray-excitable nanoparticles. Opt. Lett. 35(20), 3345–3347 (2010)

    Article  Google Scholar 

  11. Li, C., Di, K., Bec, J., Cherry, S.R.: X-ray luminescence optical tomography imaging: experimental studies. Opt. Lett. 38(13), 2339–2341 (2013)

    Article  Google Scholar 

  12. Chen, D., Zhu, S., Yi, H., Zhang, X., Chen, D., Liang, J., Tian, J.: Cone beam X-ray luminescence computed tomography: a feasibility study. Med. Phys. 40(3), 031111 (2013)

    Article  Google Scholar 

  13. Cong, W., Wang, G.: X-ray fan-beam luminescence tomography. Austin J. Biomed. Eng. 1(5), 1024 (2014)

    Google Scholar 

  14. Liu, X., Liao, Q., Wang, H., Yan, Z.: Excitation-resolved cone-beam X-ray luminescence tomography. J. Biomed. Opt. Lett. 20(7), 070501 (2015)

    Article  Google Scholar 

  15. Cong, W., Pan, Z., Filkins, R., Srivastava, A., Ishaque, N., Stefanov, P., Wang, G.: X-ray micromodulated luminescence tomography in dual-cone geometry. J. Biomed. Opt. 19(7), 076002 (2014)

    Article  Google Scholar 

  16. Zhang, J., Chen, D., Liang, J., Xue, H., Lei, J., Wang, Q., Chen, D., Meng, M., Jin, Z., Tian, J.: Incorporating MRI structural information into bioluminescence tomography: system, heterogeneous reconstruction and in vivo quantification. Biomed. Opt. Express. 5(6), 1861 (2014)

    Article  Google Scholar 

  17. Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans. Image Process. 26(10), 4753–4764 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  18. Liu, X., Liao, Q., Wang, H.: In vivo X-ray luminescence tomographic imaging with single-view data. Opt. Lett. 38(22), 4530–4533 (2013)

    Article  Google Scholar 

  19. Zhang, J., Shi, J., Cao, X., Liu, F., Bai, J., Luo, J.: Fast reconstruction of fluorescence molecular tomography via a permissible region extraction strategy. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 31(8), 1886–1894 (2014)

    Article  Google Scholar 

  20. Qin, C., Zhu, S., Feng, J., Zhong, J., Ma, X., Wu, P., Tian, J.: Comparison of permissible source region and multispectral data using efficient bioluminescence tomography method. J. Biophotonics. 4(11–12), 824–839 (2011)

    Article  Google Scholar 

  21. Shi, J., Liu, F., Zhang, J., Luo, J., Bai, J.: Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization. J. Biomed. Opt. 20(5), 055004 (2015)

    Article  Google Scholar 

  22. Seferis, I., Michail, C., Valais, I., Zeler, J., Liaparinos, P., Fountos, G., Kalyvas, N., David, S., Stromatiad, F., Zycha, E., Kandarakisc, I., Panayiotakisb, G.: Imaging performance of a thin Lu2O3:Eu nanophosphor scintillating screen coupled to a high resolution CMOS sensor under X-ray radiographic conditions: comparison with Gd2O2S:Eu conventional phosphor screen. Spie Med. Imaging, 9033(9), 90333T–90333T-6 (2014)

    Google Scholar 

  23. Klose, A.D., Ntziachristos, V., Hielscher, A.H.: The inverse source problem based on the radiative transfer equation in optical molecular imaging. J. Comput. Phys. 202(1), 323–345 (2005)

    Article  MATH  Google Scholar 

  24. Schweiger, M., Arridge, S.R., Hiraoka, M., Delpy, D.T.: The finite element method for the propagation of light in scattering media: boundary and source conditions. Thames Hudson. 106(5–6), 355–368 (1999)

    Google Scholar 

  25. Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Conf. Signal. 1, 1–3 (1995)

    Google Scholar 

  26. Dogdas, B., Stout, D., Chatziioannou, A.F., Leahy, R.M.: Digimouse: a 3D whole body mouse atlas from CT and cryosection data. Phys. Med. Biol. 52(3), 577 (2007)

    Article  Google Scholar 

  27. Cong, W.X., Shen, H.O., Wang, G.: Spectrally resolving and scattering-compensated X-ray luminescence/fluorescence computed tomography. J. Biomed. Opt. 16(6), 409–416 (2011)

    Article  Google Scholar 

  28. He, X., Dong, F., Yu, J., Guo, H., Hou, Y.: Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation. J. Opt. Soc. Am. A. 32(11), 1928–1935 (2015)

    Article  Google Scholar 

  29. Yi, H., Zhang, X., Peng, J., Zhao, F., Wang, X., Hou, Y., Chen, D., He, X.: Reconstruction for limited-projection fluorescence molecular tomography based on a double-mesh strategy. BioMed Res. Int. 2016, 5682851 (2016)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61372046, 11571012, 61601363, 61640418, 61601154, the Project funded by China Postdoctoral Science Foundation under Grant No. 2016M602851, the Science and Technology Plan Program in Shaanxi Province of China under Grant No. 2015KW-002, Scientific Research Program Funded by Shaanxi Provincial Education Department under Grant No. 16JK1772. The authors would like to thank the School of Life Science and Technology of Xidian University for providing phantom experimental data.

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  1. School of Information Sciences and Technology, Northwest University, Xi’an, 710027, Shaanxi, China

    Huangjian Yi, Xuan Qu, Yi Sun, Jinye Peng, Yuqing Hou & Xiaowei He

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Yi, H., Qu, X., Sun, Y. et al. A permissible region extraction based on a knowledge priori for X-ray luminescence computed tomography. Multimedia Systems 25, 147–154 (2019). https://doi.org/10.1007/s00530-017-0576-3

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