Skip to main content

Thank you for visiting 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.

Transcranial ultrafast ultrasound localization microscopy of brain vasculature in patients


Changes in cerebral blood flow are associated with stroke, aneurysms, vascular cognitive impairment, neurodegenerative diseases and other pathologies. Brain angiograms, typically performed via computed tomography or magnetic resonance imaging, are limited to millimetre-scale resolution and are insensitive to blood-flow dynamics. Here we show that ultrafast ultrasound localization microscopy of intravenously injected microbubbles enables transcranial imaging of deep vasculature in the adult human brain at microscopic resolution and the quantification of haemodynamic parameters. Adaptive speckle tracking to correct for micrometric brain-motion artefacts and ultrasonic-wave aberrations induced during transcranial propagation allowed us to map the vascular network of tangled arteries to functionally characterize blood-flow dynamics at a resolution of up to 25 μm and to detect blood vortices in a small deep-seated aneurysm in a patient. Ultrafast ultrasound localization microscopy may facilitate the understanding of brain haemodynamics and of how vascular abnormalities in the brain are related to neurological pathologies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Transcranial ULM of deep brain vessels in patients.
Fig. 2: Estimation of the ULM resolution in a standard imaging case.
Fig. 3: ULM characterizes haemodynamics and discriminates diastolic and systolic flow.
Fig. 4: Clinical relevance of t-ULM for a deep-seated aneurysm.

Data availability

Data examples are available from the repository with the identifier All raw and analysed data used in this study are available on request.

Code availability

Data samples and MATLAB codes regarding the most important steps of the processing routine are available from the Zenodo repository at Codes for the loading and management of data are available on request.


  1. 1.

    Liesz, A. The vascular side of Alzheimer’s disease. Science 365, 223–224 (2019).

    CAS  Article  Google Scholar 

  2. 2.

    O’Brien, J. T. & Thomas, A. Vascular dementia. Lancet 386, 1698–1706 (2015).

    Article  Google Scholar 

  3. 3.

    Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).

    CAS  Article  Google Scholar 

  4. 4.

    Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–796 (2006).

    CAS  Article  Google Scholar 

  5. 5.

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

    CAS  Article  Google Scholar 

  6. 6.

    Couture, O., Hingot, V., Heiles, B., Muleki-Seya, P. & Tanter, M. Ultrasound localization microscopy and super-resolution: a state of the art. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 65, 1304–1320 (2018).

    Article  Google Scholar 

  7. 7.

    Couture, O., Besson, B., Montaldo, G., Fink, M. & Tanter, M. Microbubble ultrasound super-localization imaging (MUSLI). In 2011 IEEE International Ultrasonics Symposium (IEEE, 2011).

  8. 8.

    Viessmann, O. M., Eckersley, R. J., Christensen-Jeffries, K., Tang, M. X. & Dunsby, C. Acoustic super-resolution with ultrasound and microbubbles. Phys. Med. Biol. 58, 6447–6458 (2013).

    CAS  Article  Google Scholar 

  9. 9.

    Siepmann, M., Schmitz, G., Bzyl, J., Palmowski, M. & Kiessling, F. Imaging tumor vascularity by tracing single microbubbles. In 2011 IEEE International Ultrasonics Symposium (IEEE, 2011).

  10. 10.

    Demené, C. et al. Ultrafast Doppler reveals the mapping of cerebral vascular resistivity in neonates. J. Cereb. Blood Flow. Metab. (2014).

  11. 11.

    Demene, C. et al. Functional ultrasound imaging of brain activity in human newborns. Sci. Transl. Med. 9, eaah6756 (2017).

    Article  Google Scholar 

  12. 12.

    Demené, C., Mairesse, J., Baranger, J., Tanter, M. & Baud, O. Ultrafast Doppler for neonatal brain imaging. NeuroImage 185, 851–856 (2019).

    Article  Google Scholar 

  13. 13.

    Imbault, M., Chauvet, D., Gennisson, J.-L., Capelle, L. & Tanter, M. Intraoperative functional ultrasound imaging of human brain activity. Sci. Rep. 7, 7304 (2017).

    Article  Google Scholar 

  14. 14.

    Soloukey, S. et al. Functional ultrasound (fUS) during awake brain surgery: the clinical potential of intra-operative functional and vascular brain mapping. Front. Neurosci. 13, 1384 (2020).

    Article  Google Scholar 

  15. 15.

    Bamber, J. C. in Physical Principles of Medical Ultrasonics (eds Hill, C. R. et al.) 93–166 (Wiley, 2005).

  16. 16.

    Tanter, M., Thomas, J.-L. & Fink, M. Time reversal and the inverse filter. J. Acoust. Soc. Am. 108, 223–234 (2000).

    CAS  Article  Google Scholar 

  17. 17.

    Zhu, Q. & Steinberg, B. D. Large-transducer measurements of wavefront distortion in the female breast. Ultrason. Imaging 14, 276–299 (1992).

    CAS  Article  Google Scholar 

  18. 18.

    Anderson, M. E., McKeag, M. S. & Trahey, G. E. The impact of sound speed errors on medical ultrasound imaging. J. Acoust. Soc. Am. 107, 3540–3548 (2000).

    CAS  Article  Google Scholar 

  19. 19.

    Fry, F. J. & Barger, J. E. Acoustical properties of the human skull. J. Acoust. Soc. Am. 63, 1576–1590 (1978).

    CAS  Article  Google Scholar 

  20. 20.

    Schneider, M. Characteristics of SonoVueTM. Echocardiography 16, 743–746 (1999).

    Article  Google Scholar 

  21. 21.

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

    Article  Google Scholar 

  22. 22.

    Papadacci, C., Pernot, M., Couade, M., Fink, M. & Tanter, M. High-contrast ultrafast imaging of the heart. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61, 288–301 (2014).

    Article  Google Scholar 

  23. 23.

    Demene, 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  Google Scholar 

  24. 24.

    Flax, S. W. & O’Donnell, M. Phase-aberration correction using signals from point reflectors and diffuse scatterers: basic principles. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 35, 758–767 (1988).

    CAS  Article  Google Scholar 

  25. 25.

    Prada, C., Wu, F. & Fink, M. The iterative time reversal mirror: a solution to self‐focusing in the pulse echo mode. J. Acoust. Soc. Am. 90, 1119–1129 (1991).

    Article  Google Scholar 

  26. 26.

    Tinevez, J.-Y. A simple particle tracking algorithm for MATLAB that can deal with gaps: tinevez/simpletracker (MathWorks, 2019).

  27. 27.

    Szabo, T. L. Diagnostic Ultrasound Imaging: Inside Out 2nd edn (Elsevier, 2014).

  28. 28.

    Paszkowiak, J. J. & Dardik, A. Arterial wall shear stress: observations from the bench to the bedside. Vasc. Endovascular Surg. (2003).

  29. 29.

    Febina, J., Sikkandar, M. Y. & Sudharsan, N. M. Wall shear stress estimation of thoracic aortic aneurysm using computational fluid dynamics. Comput. Math. Methods Med. 2018, 7126532 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    Goudot, G. et al. Wall shear stress measurement by ultrafast vector flow imaging for atherosclerotic carotid stenosis. Ultraschall Med. (2019).

  31. 31.

    Scott, R. M. & Smith, E. R. Moyamoya disease and Moyamoya syndrome. N. Engl. J. Med. 360, 1226–1237 (2009).

    CAS  Article  Google Scholar 

  32. 32.

    Lee, C.-H., Jeon, S.-H., Wang, S.-J., Shin, B.-S. & Kang, H. G. Factors associated with temporal window failure in transcranial Doppler sonography. Neurol. Sci. 41, 3293–3299 (2020).

    Article  Google Scholar 

  33. 33.

    Heiles, B. et al. Ultrafast 3D ultrasound localization microscopy using a 32 × 32 matrix array. IEEE Trans. Med. Imaging (2019).

  34. 34.

    Morel, D. Human pharmacokinetics and safety evaluation of SonoVue, a new contrast agent for ultrasound imaging. Invest. Radiol. 35, 80–85 (2000).

    CAS  Article  Google Scholar 

  35. 35.

    Martin, K. The acoustic safety of new ultrasound technologies. Ultrasound 18, 110–118 (2010).

    Article  Google Scholar 

  36. 36.

    Bigelow, T. A. et al. The thermal index. J. Ultrasound Med. 30, 714–734 (2011).

    Article  Google Scholar 

  37. 37.

    Gateau, J. et al. Transcranial ultrasonic therapy based on time reversal of acoustically induced cavitation bubble signature. IEEE Trans. Biomed. Eng. 57, 134–144 (2010).

    Article  Google Scholar 

  38. 38.

    O’Reilly, M. A. & Hynynen, K. A super-resolution ultrasound method for brain vascular mapping. Med. Phys. 40, 110701 (2013).

    Article  Google Scholar 

  39. 39.

    Soulioti, D. E., Espíndola, D., Dayton, P. A. & Pinton, G. F. Super-resolution imaging through the human skull. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67, 25–36 (2020).

    Article  Google Scholar 

  40. 40.

    Kasai, C., Namekawa, K., Koyano, A. & Omoto, R. Real-time two-dimensional blood flow imaging using an autocorrelation technique. IEEE Trans. Sonics Ultrason. 32, 458–464 (1985).

    Article  Google Scholar 

  41. 41.

    Bercoff, J., Tanter, M. & Fink, M. Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 51, 396–409 (2004).

    Article  Google Scholar 

  42. 42.

    Jerman, T., Pernuš, F., Likar, B. & Špiclin, Ž. Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Trans. Med. Imaging 35, 2107–2118 (2016).

    Article  Google Scholar 

  43. 43.

    Jerman, T., Pernuš, F., Likar, B. & Špiclin, Ž. Beyond Frangi: an improved multiscale vesselness filter. Proc. SPIE 9413, 94132A (2015).

    Google Scholar 

  44. 44.

    Frangi, A. F., Niessen, W. J., Hoogeveen, R. M., Van Walsum, T. & Viergever, M. A. Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans. Med. Imaging 18, 946–956 (1999).

    CAS  Article  Google Scholar 

  45. 45.

    Hingot, V. et al. Microvascular flow dictates the compromise between spatial resolution and acquisition time in ultrasound localization microscopy. Sci. Rep. 9, 2456 (2019).

    Article  Google Scholar 

  46. 46.

    Errico, C. et al. Transcranial functional ultrasound imaging of the brain using microbubble-enhanced ultrasensitive Doppler. NeuroImage 124, 752–761 (2016).

    Article  Google Scholar 

Download references


This work was supported by a research grant from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7/2007–2013)/ERC Advanced grant agreement no. 339244-FUSIMAGINE (M.T.), by the Swiss National Foundation (Fond National Suisse) grant no. CR3213_150654 (F.P.) and the Fondation Bettencourt–Schueller. We thank L. Puke for her help with patient recruitment. We thank the NVIDIA Corporation for their support through the NVIDIA GPU Grant program and the donation of a Titan Xp GPU used for this research.

Author information




C.D., F.P. and M.T. conceived the study. C.D., J.R. and M.P. developed the sequence acquisition and beamforming software. C.D., J.R. and F.P. acquired data. C.D., J.R., B.H., A.D., M.P. and M.T. developed data-processing algorithms. A.D. and C.D. developed the visualization algorithms. C.D., F.P. and M.T. interpreted the results. C.D. and M.T. wrote the first draft of the manuscript with substantial contribution from F.P. M.T. and F.P. co-directed the work. All authors edited and approved the final version of the manuscript.

Corresponding author

Correspondence to Mickael Tanter.

Ethics declarations

Competing interests

M.P. and M.T. are co-founders and shareholders of the Iconeus company commercializing ultrasound neuroimaging scanners. M.T. is co-inventor of the patent WO2012080614A1 on the ultrasound localization microscopy method filed on 16 December 2010 and licenced to the Iconeus company. All other authors declare no competing interests.

Additional information

Peer review information Nature Biomedical Engineering thanks James Greenleaf and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Supplementary Information

Supplementary Information

Supplementary figures and video captions.

Reporting Summary

Peer review information

Supplementary Video 1

Principles and implementation of t-ULM.

Supplementary Video 2

Comparison of t-ULM and CT scan angiography.

Supplementary Video 3

Transcranial ULM of a 63-year-old patient.

Supplementary Video 4

Transcranial ULM of a 79-year-old patient.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Demené, C., Robin, J., Dizeux, A. et al. Transcranial ultrafast ultrasound localization microscopy of brain vasculature in patients. Nat Biomed Eng 5, 219–228 (2021).

Download citation

Further reading


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