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TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography

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Image and Graphics Technologies and Applications (IGTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1910))

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Abstract

Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of luminescent sources within tissue. However, reconstructing the distribution of the quantum field from boundary measurements is a typical ill-posed problem. In this work, we propose a novel two-step reconstruction network (TSR-Net) based on a fusion mechanism, that integrates two encoder-decoder networks (ED-Net) using a concatenation block. Firstly, an ED-Net is trained to learn the CT structural features of tissues with the measured data. Then, the trained ED-Net is fixed and cascaded by another ED-Net for a second-step training to predict the 3D distributions. Numerical simulations reveal that the proposed approach can not only accurately reconstruct the intensity values of the luminescent sources, but also achieve a reconstruction resolution of 1mm with low target-background contrast. Furthermore, the well-trained network is still effective in the reconstruction of tissues with different shapes, which indicates an excellent generalization ability of the algorithm.

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References

  1. Ruggiero, A., Holland, J.P., Lewis, J.S.: Cerenkov luminescence imaging of medical isotopes. J. Nucl. Med. 51(7), 1123–1130 (2010)

    Article  Google Scholar 

  2. Pogue, B.W., et al.: Map of in vivo oxygen pressure with submillimeter resolution and nanomolar sensitivity enabled by cherenkov-exited luminescence scanned imaging. Nat. Biomed. Eng. 2(4), 254–264 (2018)

    Article  Google Scholar 

  3. Brůža, P., Lin, H., Vinogradov, S.A., Jarvis, L.A., Gladstone, D.J., Pogue, B.W.: Light sheet luminescence imaging with Cherenkov excitation in thick scattering media. Opt. Lett. 41(13), 2986–2989 (2016)

    Article  Google Scholar 

  4. Tanha, K., Pashazadeh, A.M., Pogue, B.W.: Review of biomedical Čerenkov luminescence imaging applications. Opt. Express 6(8), 3053–3065 (2015)

    Article  Google Scholar 

  5. Lin, H., et al.: Comparison of Cherenkov excited fluorescence and phosphorescence molecular sensing from tissue with external beam irradiation. Phys. Med. Biol. 61(10), 3955–3968 (2016)

    Article  Google Scholar 

  6. Feng, J., Bruza, P., Dehghani, H., Davis, S.C., Pogue, B.W.: Cherenkov-excited luminescence sheet imaging (CELSI) tomographic reconstruction. In: Proceedings of SPIE, vol. 10049, p. 1004912 (2017)

    Google Scholar 

  7. Shi, J., Liu, F., Zhang, G., Luo, J., Bai, J.: Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm. J. Biomed. Opt. 19(4), 046018 (2014)

    Article  Google Scholar 

  8. Zhao, L., Yang, H., Cong, W., Wang, G., Intes, X.: LP regularization for early gate fluorescence molecular tomography. Opt. Lett. 39(14), 4156–4159 (2014)

    Article  Google Scholar 

  9. Shi, J., Zhang, B., Liu, F., Luo, J., Bai, J.: Efficient L1 regularization based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient. Opt. Lett. 38(18), 3696–3699 (2012)

    Article  Google Scholar 

  10. Lu, W., Duan, J., Miguel, D.O., Herve, L., Styles, L.B.: Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography. Biomed. Opt. Express 10(6), 2684–2707 (2019)

    Article  Google Scholar 

  11. Feng, J., et al.: Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography. Optica 9, 264–267 (2022)

    Article  MathSciNet  Google Scholar 

  12. Zhang, W., et al.: Selfrec-net: self-supervised deep learning approach for the reconstruction of cherenkov-excited luminescence scanned tomography. Biomed. Opt. Express 14, 783–798 (2023)

    Article  Google Scholar 

  13. Yoo, J., et al.: Deep learning diffuse optical tomography. IEEE Trans. Med. Imaging 39(4), 877–887 (2020)

    Article  Google Scholar 

  14. Guo, L., Liu, F., Cai, C., Liu, J., Zhang, G.: 3D deep encoder-decoder network for fluorescence molecular tomography. Opt. Lett. 44(8), 1892–1895 (2019)

    Article  Google Scholar 

  15. Liao, M., Zheng, S., Lu, D., Situ, G., Peng, X.: Real-time imaging through moving scattering layers via a two-step deep learning strategy. In: Proceedings of SPIE, vol. 11351, p. 113510V (2020)

    Google Scholar 

  16. Zhu, S., Guo, E., Gu, J., Bai, L., Han, J.: Imaging through unknown scattering media based on physics-informed learning. Photonics Res. 9(5), B210–B219 (2021)

    Article  Google Scholar 

  17. Shang, R., Hoffer-Hawlik, K., Wang, F., Situ, G., Luke, G.P.: Two-step training deep learning framework for computational imaging without physics priors. Opt. Express 29, 15239–15254 (2021)

    Article  Google Scholar 

  18. Belthangady, C., Royer, L.A.: Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods 16(12), 1215–1225 (2019)

    Article  Google Scholar 

  19. Weigert, M., et al.: Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15(12), 1090–1097 (2018)

    Article  Google Scholar 

  20. Shimobaba, T., et al.: Computational ghost imaging using deep learning. Opt. Commun. 413, 147–151 (2018)

    Article  Google Scholar 

  21. Dehghani, H., et al.: Near infrared optical tomography using NIRFAST: algorithm for numerical model and image reconstruction. Commun. Num. Methods Eng. 25(6), 711–732 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  22. Soubret, A., Ripoll, J., Ntziachristos, V.: Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized born ratio. IEEE Trans. Med. Imaging 24(10), 1377–1386 (2005)

    Article  Google Scholar 

  23. Arridge, S.R.: Optical tomography in medical imaging. Inverse Probl. 15(2), 41–93 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  24. Cong, A.X., Wang, G.: A finite-element-based reconstruction Method for 3D fluorescence tomography. Opt. Express 13(24), 9847–9857 (2005)

    Article  Google Scholar 

  25. Ding, X., Guo, Y., Ding, G., Han, J.: ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks. In: 2019 IEEE International Conference on Computer Vision, ICCV, Seoul, pp. 1911–1920 (2019)

    Google Scholar 

  26. Çiçek, Ö., Abdulkadir, A., Lienkamp, S., Brox, T., Ronneberger, O.: 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  27. Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Salt Lake City, pp. 1664–1673 (2018)

    Google Scholar 

  28. Jermyn, M., et al.: Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography. J. Biomed. Opt. 18(8), 086007 (2013)

    Article  Google Scholar 

  29. Paszke, A., et al.: Automatic differentiation in PyTorch. In: Advances in Neural Information Processing Systems (NIPS), Long Beach (2017)

    Google Scholar 

  30. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)

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Acknowledgments

This paper is supported by the Project for the National Natural Science Foundation of China (82171992, 62105010).

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Authors and Affiliations

  1. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Wenqian Zhang, Jinchao Feng, Zhe Li, Zhonghua Sun & Kebin Jia

  2. Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China

    Wenqian Zhang, Jinchao Feng, Zhe Li, Zhonghua Sun & Kebin Jia

Authors
  1. Wenqian Zhang
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  2. Jinchao Feng
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  3. Zhe Li
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  4. Zhonghua Sun
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  5. Kebin Jia
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Corresponding author

Correspondence to Jinchao Feng .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Wang Yongtian

  2. Beijing University of Technology, Beijing, China

    Wu Lifang

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Zhang, W., Feng, J., Li, Z., Sun, Z., Jia, K. (2023). TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_3

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  • DOI: https://doi.org/10.1007/978-981-99-7549-5_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7548-8

  • Online ISBN: 978-981-99-7549-5

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