Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Photoacoustic vector tomography for deep haemodynamic imaging

Nature Biomedical Engineering (2023)Cite this article

Subjects

Abstract

Imaging deep haemodynamics non-invasively remains a quest. Although optical imaging techniques can be used to measure blood flow, they are generally limited to imaging within ∼1 mm below the skin’s surface. Here we show that such optical diffusion limit can be broken through by leveraging the spatial heterogeneity of blood and its photoacoustic contrast. Specifically, successive single-shot wide-field photoacoustic images of blood vessels can be used to visualize the frame-to-frame propagation of blood and to estimate blood flow speed and direction pixel-wise. The method, which we named photoacoustic vector tomography (PAVT), allows for the quantification of haemodynamics in veins more than 5 mm deep, as we show for regions in the hands and arms of healthy volunteers. PAVT may offer advantages for the diagnosis and monitoring of vascular diseases and for the mapping of the function of the circulatory system.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: PAVT.
Fig. 2: System characterization and phantom validation.
Fig. 3: PAVT vector flow maps.
Fig. 4: PAVT characterization of haemodynamics around a valve.
Fig. 5: Measuring functional responses to a blood-pressure cuff.

Similar content being viewed by others

Data availability

The data supporting the findings of this study are provided within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are available for research purposes from the corresponding author on reasonable request.

Code availability

The reconstruction codes based on the universal back-projection algorithm are proprietary and used in licensed technologies, yet they are available from the corresponding author on reasonable request.

References

  1. Won, R. Mapping blood flow. Nat. Photonics 5, 393 (2011).

    Article  CAS  Google Scholar 

  2. Rajan, V., Varghese, B., van Leeuwen, T. G. & Steenbergen, W. Review of methodological developments in laser Doppler flowmetry. Lasers Med. Sci. 24, 269–283 (2009).

    Article  PubMed  Google Scholar 

  3. Leitgeb, R. A., Werkmeister, R. M., Blatter, C. & Schmetterer, L. Doppler Optical Coherence Tomography. Prog. Retin. Eye Res. 41, 26–43 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Qureshi, M. M. et al. Quantitative blood flow estimation in vivo by optical speckle image velocimetry: publisher’s note. Optica 8, 1326 (2021).

    Article  Google Scholar 

  5. Boas, D. A. & Dunn, A. K. Laser speckle contrast imaging in biomedical optics. J. Biomed. Opt. 15, 011109 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Cinotti, E. et al. Quantification of capillary blood cell flow using reflectance confocal microscopy. Skin Res. Technol. 20, 373–378 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Wang, L. V. & Hu, S. Photoacoustic tomography: in vivo imaging from organelles to organs. Science 335, 1458–1462 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang, L. V. Tutorial on photoacoustic microscopy and computed tomography. IEEE J. Sel. Top. Quantum Electron. 14, 171–179 (2008).

    Article  Google Scholar 

  9. Yao, J. et al. High-speed label-free functional photoacoustic microscopy of mouse brain in action. Nat. Methods 12, 407–410 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kinnunen, M., Kauppila, A., Karmenyan, A. & Myllylä, R. Effect of the size and shape of a red blood cell on elastic light scattering properties at the single-cell level. Biomed. Opt. Express 2, 1803–1814 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wang, L. V. Multiscale photoacoustic microscopy and computed tomography. Nat. Photonics 3, 503–509 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Brunker, J. & Beard, P. Velocity measurements in whole blood using acoustic resolution photoacoustic Doppler. Biomed. Opt. Express 7, 2789–2806 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yao, J. & Wang, L. V. Photoacoustic brain imaging: from microscopic to macroscopic scales. Neurophotonics 1, 011003 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Guo, Z., Li, L. & Wang, L. V. On the speckle-free nature of photoacoustic tomography. Med. Phys. 36, 4084–4088 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Pakdaman Zangabad, R. et al. Photoacoustic flow velocity imaging based on complex field decorrelation. Photoacoustics 22, 100256 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Xu, M. & Wang, L. V. Universal back-projection algorithm for photoacoustic computed tomography. Phys. Rev. E 71, 016706 (2005).

    Article  Google Scholar 

  17. Lurie, F., Kistner, R. L., Eklof, B. & Kessler, D. Mechanism of venous valve closure and role of the valve in circulation: a new concept. J. Vasc. Surg. 38, 955–961 (2003).

    Article  PubMed  Google Scholar 

  18. Szabo, T. L. Diagnostic Ultrasound Imaging: Inside Out (Academic Press, 2004).

  19. Tanter, M. & Fink, M. Ultrafast imaging in biomedical ultrasound. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61, 102–119 (2014).

    Article  PubMed  Google Scholar 

  20. Errico, C. et al. Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging. Nature 527, 499–502 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, L. V. & Wu, H. Biomedical Optics: Principles and Imaging (John Wiley & Sons, 2012).

  22. American National Standards Institute. American National Standard for the Safe Use of Lasers ANSI z136.1–2014 (Laser Institute of America, 2014).

  23. Cramer, H., & Grenader, U. The Nyquist frequency is that frequency whose period is two sampling intervals. Probability and Statistics: The Harald Cramer Volume p. 434 (Almqvist & Wiksell, 1959).

  24. Lee, Y., Kang, J. & Yoo, Y. Automatic dynamic range adjustment for ultrasound B-mode imaging. Ultrasonics 56, 435–443 (2015).

    Article  PubMed  Google Scholar 

  25. Fernández-Colino, A. & Jockenhoevel, S. Advances in engineering venous valves: the pursuit of a definite solution for chronic venous disease. Tissue Eng. B Rev. 27, 253–265 (2021).

    Article  Google Scholar 

  26. Petrila, T. & Trif, D. Basics of Fluid Mechanics and Introduction to Computational Fluid Dynamics (Springer Science & Business Media, 2004).

  27. Murray, C. D. The physiological principle of minimum work. Proc. Natl Acad. Sci. USA 12, 207–214 (1926).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wiedeman, M. P. Dimensions of blood vessels from distributing artery to collecting vein. Circ. Res. 12, 375–378 (1963).

    Article  CAS  PubMed  Google Scholar 

  29. Li, L. et al. Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution. Nat. Biomed. Eng. 1, 0071 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Keys, A. The oxygen saturation of the venous blood in normal human subjects. Am. J. Physiol. Leg. Content 124, 13–21 (1938).

    Article  CAS  Google Scholar 

  31. Na, S., Zhang, Y. & Wang, L. V. Cross-ray ultrasound tomography and photoacoustic tomography of cerebral haemodynamics in rodents. Adv. Sci. 9, 2201104 (2022).

    Article  Google Scholar 

  32. Yao, J., Maslov, K. I. & Wang, L. V. In vivo photoacoustic tomography of total blood flow and potential imaging of cancer angiogenesis and hypermetabolism. Technol. Cancer Res. Treat. 11, 301–307 (2012).

    Article  PubMed  Google Scholar 

  33. Beebe-Dimmer, J. L., Pfeifer, J. R., Engle, J. S. & Schottenfeld, D. The epidemiology of chronic venous insufficiency and varicose veins. Ann. Epidemiol. 15, 175–184 (2005).

    Article  PubMed  Google Scholar 

  34. Lin, L. & Wang, L. V. The emerging role of photoacoustic imaging in clinical oncology. Nat. Rev. Clin. Oncol. 19, 365–384 (2022).

    Article  PubMed  Google Scholar 

  35. Na, S. et al. Massively parallel functional photoacoustic computed tomography of the human brain. Nat. Biomed. Eng. 6, 584–592 (2022).

  36. Zhang, Y. et al. Transcranial photoacoustic computed tomography of human brain function. Preprint at https://arxiv.org/abs/2206.00248 (2022).

  37. Yao, J., Maslov, K. I., Zhang, Y., Xia, Y. & Wang, L. V. Label-free oxygen-metabolic photoacoustic microscopy in vivo. J. Biomed. Opt. 16, 076003 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Montaldo, G., Tanter, M., Bercoff, J., Benech, N. & Fink, M. Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 489–506 (2009).

    Article  PubMed  Google Scholar 

  39. Zhang, Y., Guo, Y. & Lee, W.-N. Ultrafast ultrasound imaging with cascaded dual-polarity waves. IEEE Trans. Med. Imaging 37, 906–917 (2018).

    Article  PubMed  Google Scholar 

  40. Yiu, B. Y., Lai, S. S. & Alfred, C. H. Vector projectile imaging: time-resolved dynamic visualization of complex flow patterns. Ultrasound Med. Biol. 40, 2295–2309 (2014).

    Article  PubMed  Google Scholar 

  41. Dong, J., Zhang, Y. & Lee, W.-N. Walled vessel-mimicking phantom for ultrasound imaging using 3D printing with a water-soluble filament: design principle, fluid-structure interaction (FSI) simulation, and experimental validation. Phys. Med. Biol. 65, 085006 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Demené, C. et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases Doppler and fultrasound sensitivity. IEEE Trans. Med. Imaging 34, 2271–2285 (2015).

    Article  PubMed  Google Scholar 

  43. Farnebäck, G. Two-frame motion estimation based on polynomial expansion. In Scandinavian Conference on Image Analysis 363–370 (Springer, 2003).

  44. Leis, J. W. Digital Signal Processing Using MATLAB for Students and Researchers (John Wiley & Sons, 2011).

Download references

Acknowledgements

We thank K. Maslov, L. Li and R. Cao for discussions about the flow mechanism; S. L. Spitalnik and P. Buehler for discussions about blood physiology; S. Davis and B. Park for discussion on the potential improvement of flow visualization; and L. Li for the suggestion about fibre coupling and assistance in light alignment. This work was sponsored by the United States National Institutes of Health (NIH) grants U01 EB029823 (BRAIN Initiative) and R35 CA220436 (Outstanding Investigator Award).

Author information

Author notes

  1. These authors contributed equally: Yang Zhang, Joshua Olick-Gibson.

Authors and Affiliations

  1. Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA

    Yang Zhang, Joshua Olick-Gibson, Anjul Khadria & Lihong V. Wang

  2. Caltech Optical Imaging Laboratory, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA

    Lihong V. Wang

Authors
  1. Yang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua Olick-Gibson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anjul Khadria
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lihong V. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.V.W., Y.Z. and J.O.-G. designed the study. Y.Z. and J.O.-G. built the system, analysed the data and wrote the paper with input from all authors. Y.Z., J.O.-G. and A.K. performed the experiments. L.V.W., Y.Z., J.O.-G. and A.K. interpreted the data. L.V.W. supervised the study and revised the paper.

Corresponding author

Correspondence to Lihong V. Wang.

Ethics declarations

Competing interests

L.V.W. has a financial interest in Microphotoacoustics Inc., CalPACT LLC and Union Photoacoustic Technologies Ltd. These companies did not provide support for this work. All other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Jan Grimm, Ben McLarney, Chengbo Liu and Liming Nie for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Main Supplementary Information

Supplementary figures and video captions.

Reporting Summary

Supplementary Video 1

Visualization of blood flow in vivo.

Supplementary Video 2

Phantom validation.

Supplementary Video 3

In vivo blood flow of a vessel with a varying diameter.

Supplementary Video 4

In vivo blood flow of a vessel at an isosbestic wavelength.

Supplementary Video 5

In vivo blood flow at different wavelengths.

Supplementary Video 6

In vivo blood flow of a vessel at a depth of 3.5 mm.

Supplementary Video 7

In vivo blood flow of a vessel at a depth of 5.5 mm.

Supplementary Video 8

In vivo haemodynamics around a valve.

Supplementary Video 9

Measuring functional responses to a blood-pressure cuff.

Supplementary Video 10

Measuring blood flow in artery #1.

Supplementary Video 11

Measuring blood flow in artery #2.

Supplementary Video 12

Imaging time-variant blood flow.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Olick-Gibson, J., Khadria, A. et al. Photoacoustic vector tomography for deep haemodynamic imaging. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01148-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41551-023-01148-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing