Article | Published:

Computational time-of-flight diffuse optical tomography

Abstract

Imaging through a strongly diffusive medium remains an outstanding challenge, in particular in applications in biological and medical imaging. Here, we propose a method based on a single-photon time-of-flight camera that allows, in combination with computational processing of the spatial and full temporal photon distribution data, imaging of an object embedded inside a strongly diffusive medium over more than 80 transport mean free paths. The technique is contactless and requires 1 s acquisition times, thus allowing Hz frame rate imaging. The imaging depth corresponds to several centimetres of human tissue and allows us to perform deep-body imaging as a proof of principle.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

All data used in this work are available from https://doi.org/10.5525/gla.researchdata.642

Code availability

All codes used in this work are available from https://doi.org/10.5525/gla.researchdata.642

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Katz, O., Heidmann, P., Fink, M. & Gigan, S. Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nat. Photon. 8, 784–790 (2014).

  2. 2.

    Woo, S. et al. Three-dimensional imaging of macroscopic objects hidden behind scattering media using time-gated aperture synthesis. Opt. Express 25, 32722–32731 (2017).

  3. 3.

    Satat, G., Tancik, M., Gupta, O., Heshmat, B. & Raskar, R. Object classification through scattering media with deep learning on time resolved measurement. Opt. Express 25, 17466–17479 (2017).

  4. 4.

    Wang, L. et al. Ballistic 2-D imaging through scattering walls using an ultrafast optical Kerr gate. Science 253, 769 (1991).

  5. 5.

    Konecky, S. D. et al. Imaging complex structures with diffuse light. Opt. Express 16, 5048 (2008).

  6. 6.

    Durduran, T., Choe, R., Baker, W. B. & Yodh, A. G. Diffuse optics for tissue monitoring and tomography. Rep. Prog. Phys. 73, 076701 (2010).

  7. 7.

    Pifferi, A. et al. New frontiers in time-domain diffuse optics, a review. J. Biomed. Opt. 21, 091310 (2016).

  8. 8.

    Shi, L. & Alfano, R. R. Deep Imaging in Tissue and Biomedical Materials: Using Linear and Nonlinear Optical Methods (Pan Stanford, 2017).

  9. 9.

    Jacques, S. L. Optical properties of biological tissues: a review. Phys. Med. Biol. 58, R37–R61 (2013).

  10. 10.

    Delpy, D. T. et al. Estimation of optical pathlength through tissue from direct time of flight measurement. Phys. Med. Biol. 33, 1433–1442 (1988).

  11. 11.

    Patterson, B., Chance, M. S. & Wilson, B. C. Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties. Appl. Opt. 28, 2331–2336 (1989).

  12. 12.

    Jacques, S. L. Time resolved propagation of ultrashort laser pulses within turbid tissues. Appl. Opt. 28, 2223–2229 (1989).

  13. 13.

    Hebden, J. C. Evaluating the spatial resolution performance of a time-resolved optical imaging system. Med. Phys. 19, 1081–1087 (1992).

  14. 14.

    Hebden, J. C., Hall, D. J. & Delpy, D. T. The spatial resolution performance of a time-resolved optical imaging system using temporal extrapolation. Med. Phys. 22, 201–208 (1995).

  15. 15.

    Gibson, A. P. & Dehghani, A. Diffuse optical imaging. Philos. Trans. R. Soc. A 367, 3055–3072 (2009).

  16. 16.

    Berg, R., Jarlman, O. & Svanberg, S. Medical transillumination imaging using short-pulse diode lasers. Appl. Opt. 32, 574–579 (1993).

  17. 17.

    Grosenick, D., Wabnitz, H., Rinneberg, H. H., Moesta, K. T. & Schlag, P. M. Development of a time-domain optical mammograph and first in vivo applications. Appl. Opt. 38, 2927–2943 (1999).

  18. 18.

    Boas, D. A. et al. Imaging the body with diffuse optical tomography. IEEE Signal Process. Mag. 18, 57–75 (2001).

  19. 19.

    Torricelli, A. et al. Time domain functional NIRS imaging for human brain mapping. Neuroimage 85, 28–50 (2014).

  20. 20.

    Eggebrecht, A. T. et al. Mapping distributed brain function and networks with diffuse optical tomography. Nat. Photon. 8, 448–454 (2014).

  21. 21.

    Dalla Mora, A. et al. Towards next-generation time-domain diffuse optics for extreme depth penetration and sensitivity. Biomed. Opt. Express 6, 1749–1760 (2015).

  22. 22.

    Pavia, J. M., Wolf, M. & Charbon, E. Single-photon avalanche diode imagers applied to near-infrared imaging. IEEE J. Sel. Top. Quantum Electron. 20, 3800908 (2014).

  23. 23.

    Gibson, A. P., Hebden, J. C. & Arridge, S. R. Recent advances in diffuse optical imaging. Phys. Med. Biol. 50, R1–R43 (2005).

  24. 24.

    Ripoll, J., Nieto-Vesperinas, M. & Carminati, R. Spatial resolution of diffuse photon density waves. J. Opt. Soc. Am. A 16, 1466 (1999).

  25. 25.

    Azizi, L., Zarychta, K., Ettori, D., Tinet, E. & Tualle, J.-M. Ultimate spatial resolution with diffuse optical tomography. Opt. Express 17, 12132 (2009).

  26. 26.

    Satat, G., Heshmat, B., Raviv, D. & Raskar, R. All photons imaging through volumetric scattering. Sci. Rep. 6, 33946 (2016).

  27. 27.

    Cai, W. et al. Time-resolved optical diffusion tomographic image reconstruction in highly scattering turbid media. Proc. Natl Acad. Sci. USA 93, 13561–13564 (1996).

  28. 28.

    Gariepy, G. et al. Single-photon sensitive light-in-fight imaging. Nat. Commun. 6, 6021 (2015).

  29. 29.

    Yoo, K., Liu, F. & Alfano, R. When does the diffusion approximation fail to describe photon transport in random media? Phys. Rev. Lett. 64, 2647 (1990).

  30. 30.

    Wang, L. V. & Wu, H.-I. in Biomedical Optics: Principles and Imaging Ch. 8, 249–281 (Wiley, 2007).

  31. 31.

    Jacques, S. L. Optical properties of biological tissues: a review. Phys. Med. Biol. 58, 5007–5008 (2013).

  32. 32.

    Gyongy, I. et al. A 256 × 256, 100-kfps, 61% fill-factor SPAD image sensor for time-resolved microscopy applications. IEEE Trans. Electron. Dev. 65, 547 (2018).

  33. 33.

    Combettes, P. L. & Pesquet, J.-C. in Fixed-Point Algorithms for Inverse Problems in Science and Engineering (eds Bauschke, H. H. et al.) Ch. 10, 185–212 (Springer, 2011).

  34. 34.

    Komodakis, N. & Pesquet, J.-C. Playing with duality: an overview of recent primal-dual approaches for solving large-scale optimization problems. IEEE Signal Process. Mag. 32, 31–54 (2015).

  35. 35.

    Mallat, S. A. Wavelet Tour of Signal Processing 2nd edn (Academic Press, 2009).

  36. 36.

    Rudin, L. I., Osher, S. & Fatemi, E. Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992).

  37. 37.

    Berisha, S. & Nagy, J. G. in Academic Press Library in Signal Processing Vol. 4 (eds Chellappa, R. & Theodoridis, S.) 193–247 (Elsevier, 2014).

Download references

Acknowledgements

D.F. acknowledges financial support the Engineering and Physical Sciences Research Council, UK (grants EP/M006514/1 and EP/M01326X/1). Y.W. acknowledges financial support from the Engineering and Physical Sciences Research Council, UK (grants EP/M008843/1 and EP/M011089/1).

Author information

A.L. and A.B. performed experiments and data analysis. A.R., F.T. and Y.W. developed the retrieval algorithms and performed data analysis. The project was devised and led by D.F. R.H. developed the SPAD camera. All authors contributed to the preparation of the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Daniele Faccio.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark
Fig. 1: Experimental set-up.
Fig. 2: Main experimental results.
Fig. 3: Single-pixel temporal histograms of the photon arrivals transmitted through 2.5 cm (red) and 5 cm (blue) of material.
Fig. 4: Tracking of a hidden object positioned at different positions inside the diffusive medium.
Fig. 5: Numerical simulations of the reconstruction of a hidden object (0.5 mm thickness, 5 mm height, separated by 1 mm).
Fig. 6: Experimental results.