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Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy

Nature Biomedical Engineering volume 6, pages 605–616 (2022)Cite this article

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An Addendum to this article was published on 20 October 2023

Abstract

Ultrafast ultrasound localization microscopy can be used to detect the subwavelength acoustic scattering of intravenously injected microbubbles to obtain haemodynamic maps of the vasculature of animals and humans. The quality of the haemodynamic maps depends on signal-to-noise ratios and on the algorithms used for the localization of the microbubbles and the rendering of their trajectories. Here we report the results of benchmarking of the performance of seven microbubble-localization algorithms. We used metrics for localization errors, localization success rates, processing times and a measure of the reprojection of the localization of the microbubbles on the original beamformed grid. We combined eleven metrics into an overall score and tested the algorithms in three simulated microcirculation datasets, and in angiography datasets of the brain of a live rat after craniotomy, an excised rat kidney and a mammary tumour in a live mouse. The algorithms, metrics and datasets, which we have made openly available at https://github.com/AChavignon/PALA and https://doi.org/10.5281/zenodo.4343435, will facilitate the identification or generation of optimal microbubble-localization algorithms for specific applications.

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Fig. 1: Rat brain vascular imaging with in vivo ULM and post-mortem microangio-CT.
Fig. 2: Description of the framework for data simulation and performance assessment.
Fig. 3: Results of the study combined in the PALA global score for each algorithm.
Fig. 4: Directional errors and statistical indices for performance on the dataset ‘in silico flow’ at 30 dB.
Fig. 5: Study of PSF non-uniformity in direction and its effect on localization.
Fig. 6: 2D ULM density-based renderings of ‘in silico flow’ along with a focus on particular areas.
Fig. 7: 2D ULM density-based renderings of ‘in vivo rat brain perfusion’, with gridding index and saturation.
Fig. 8: 2D ULM density-based renderings of three other in vivo datasets, with partial scores.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. All data generated in this study, including source data and the data used to make the figures, are available from Zenodo at https://doi.org/10.5281/zenodo.4343435.

Code availability

All codes used for data acquisition and for data analysis are available on GitHub at https://github.com/AChavignon/PALA.

References

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

    Article  PubMed  Google Scholar 

  2. Macé, E. et al. Functional ultrasound imaging of the brain. Nat. Methods 8, 662–664 (2011).

    Article  PubMed  Google Scholar 

  3. Macé, E. et al. High sensitivity brain angiography using ultrafast Doppler. IEEE Int. Ultrason. Symp. https://doi.org/10.1109/ultsym.2010.5935810 (2010).

  4. 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  PubMed  Google Scholar 

  5. Frinking, P., Segers, T., Luan, Y. & Tranquart, F. Three decades of ultrasound contrast agents: a review of the past, present and future improvements. Ultrasound Med. Biol. 46, 892–908 (2020).

    Article  PubMed  Google Scholar 

  6. Couture, O., Besson, B., Montaldo, G., Fink, M. & Tanter, M. Microbubble ultrasound super-localisation imaging (MUSLI). IEEE Int. Ultrason. Symp. https://doi.org/10.1109/ULTSYM.2011.6293576 (2011).

  7. Couture, O., Tanter, M. & Fink, M. Method and device for sound imaging. Patent 889 Cooperation Treaty (PCT)/FR2011/052810 (2011).

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  10. Christensen-Jeffries, K., Browning, R. J., Tang, M.-X., Dunsby, C. & Eckersley, R. J. In vivo acoustic super-resolution and super-resolved velocity mapping using microbubbles. IEEE Trans. Med. Imaging 34, 433–440 (2015).

    Article  PubMed  Google Scholar 

  11. Heiles, B. et al. Volumetric ultrasound localization microscopy of the whole brain microvasculature. Preprint at bioRxiv https://doi.org/10.1101/2021.09.17.460797 (2021).

  12. 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).

    Article  CAS  PubMed  Google Scholar 

  13. Lin, F. et al. 3-D ultrasound localisation microscopy for identifying microvascular morphology features of tumour angiogenesis at a resolution beyond the diffraction limit of conventional ultrasound. Theranostics 7, 196–204 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. OˈReilly, M. A. & Hynynen, K. A super-resolution ultrasound method for brain vascular mapping: super-resolution ultrasound method for brain vascular mapping. Med. Phys. 40, 110701 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Song, P. et al. Improved super-resolution ultrasound microvessel imaging with spatiotemporal nonlocal means filtering and bipartite graph-based microbubble tracking. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 65, 149–167 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Brown, J. et al. Investigation of microbubble detection methods for super-resolution imaging of microvasculature. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 66, 676–691 (2019).

    Article  PubMed  Google Scholar 

  17. Christensen-Jeffries, K. et al. Microbubble axial localisation errors in ultrasound super-resolution imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 64, 1644–1654 (2017).

    Article  PubMed  Google Scholar 

  18. Kanoulas, E. et al. Super-resolution contrast-enhanced ultrasound methodology for the identification of in vivo vascular dynamics in 2D. Invest. Radiol. 54, 500–516 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Song, P., Manduca, A., Trzasko, J. D., Daigle, R. E. & Chen, S. On the effects of spatial sampling quantisation in super-resolution ultrasound microvessel imaging. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 65, 2264–2276 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kuhn, H. W. The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2, 83–97 (1955).

    Article  Google Scholar 

  22. Zhang, G. et al. Acoustic wave sparsely activated localisation microscopy (AWSALM): super-resolution ultrasound imaging using acoustic activation and deactivation of nanodroplets. Appl. Phys. Lett. 113, 014101 (2018).

    Article  Google Scholar 

  23. Lowerison, M. R., Huang, C., Lucien, F., Chen, S. & Song, P. Ultrasound localisation microscopy of renal tumor xenografts in chicken embryo is correlated to hypoxia. Sci. Rep. 10, 2478 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Forsberg, F., Leeman, S. & Jensen, J. A. Assessment of hybrid speckle reduction algorithms. Phys. Med. Biol. 36, 1539–1549 (1991).

    Article  CAS  PubMed  Google Scholar 

  25. Ledoux, L. A. F., Brands, P. J. & Hoeks, A. P. G. Reduction of the clutter component in doppler ultrasound signals based on singular value decomposition: a simulation study. Ultrason. Imaging 19, 1–18 (1997).

    Article  CAS  PubMed  Google Scholar 

  26. Desailly, Y. et al. Contrast enhanced ultrasound by real-time spatiotemporal filtering of ultrafast images. Phys. Med. Biol. 62, 31–42 (2017).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The project was funded by the European Research Council under the European Union Horizon H2020 programme/ERC Consolidator grant agreement no. 772786-ResolveStroke. The project was also supported by Agence Nationale de la Recherche (ANR), within the project ANR Predic and the Plan Cancer UICT. We thank the laboratories Institut Langevin and PhysMed Paris for technical support and particularly L. Rahal for helping with data acquisition; C. Orset (INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders, GIP Cyceron, Institut Blood and Brain @ Caen-Normandie (BB@C), Caen, France) for the preparation of the animals and the perfusion of the contrast agent, and the biomedical imaging platform CYCERON (UMS 3408 Unicaen/CNRS, Caen, France), particularly H. Skeif and V. Beaudouin, for the micro-CT of brains; R. Hlushchuk for all his valuable advice for µAngiofil perfusion; D. Maresca for useful discussion, and our collaborators at Plateforme d’Imagerie du Vivant, Institut Cochin (U1016, INSERM Paris, France); and F. Lager, G. Renault and A. Nicolas-Boluda for helping with the mouse tumour model and the in vivo experiments.

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Author notes

  1. These authors contributed equally: Baptiste Heiles, Arthur Chavignon.

Authors and Affiliations

  1. Sorbonne Université, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, Paris, France

    Baptiste Heiles, Arthur Chavignon, Vincent Hingot & Olivier Couture

  2. ESPCI, CNRS, INSERM, PhysMedParis, Paris, France

    Baptiste Heiles, Vincent Hingot, Pauline Lopez & Eliott Teston

  3. Institut Cochin, INSERM U1016, Paris, France

    Pauline Lopez

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  1. Baptiste Heiles
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  6. Olivier Couture
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Contributions

All authors contributed to the conception of the project. E.T. and P.L. were the main collaborators on the kidney and tumour experiments. B.H. and V.H. laid out the general framework and algorithm for ULM, with contributions from all authors. B.H. wrote the original codes for the localization algorithms, with significant improvements introduced by A.C. A.C. wrote the simulation framework and implemented the metrics. V.H., A.C., E.T. and P.L. acquired in vivo data. B.H. wrote the manuscript, with input from all authors. O.C. directed the work of B.H., A.C. and V.H. as thesis advisor or postdoctoral supervisor.

Corresponding author

Correspondence to Baptiste Heiles.

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Competing interests

O.C. is a co-inventor of an ultrasound super-resolution patent ((PCT)/FR2011/052810). All other authors declare no competing interests.

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

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Supplementary information

Supplementary Information

Supplementary methods, figures and tables.

Reporting Summary

Supplementary Data

Dynamic table for calculating the global score for the performance assessment of microbubble-localization algorithms.

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Heiles, B., Chavignon, A., Hingot, V. et al. Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy. Nat. Biomed. Eng 6, 605–616 (2022). https://doi.org/10.1038/s41551-021-00824-8

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