Skip to main content

Advertisement

SpringerLink
Log in

Catheter navigation support for mechanical thrombectomy guidance: 3D/2D multimodal catheter-based registration with no contrast dye fluoroscopy

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The fusion of pre-operative imaging and intra-operative fluoroscopy may support physicians during mechanical thrombectomy for catheter navigation from the aortic arch to carotids. Nevertheless, the aortic arch volume is too important for intra-operative contrast dye injection leading to a lack of common anatomical structure of interest that results in a challenging 3D/2D registration. The objective of this work is to propose a registration method between pre-operative 3D image and no contrast dye intra-operative fluoroscopy.

Methods

The registration method exploits successive 2D fluoroscopic images of the catheter navigating in the aortic arch. The similarity measure is defined as the normalized cross-correlation between a binary combination of catheter images and a pseudo-DRR resulting from the 2D binary projection of the pre-operative 3D image (MRA or CTA). The 3D/2D transformation is decomposed in out-plane and in-plane transformations to reduce computational complexity. The 3D/2D transformation is then obtained by maximizing the similarity measure through multiresolution exhaustive search.

Results

We evaluated the registration performance through dice score and mean landmark error. We evaluated the influence of parameters setting, aortic arch type and 2D navigation sequence duration. Results on a physical phantom and data from a patient who underwent a mechanical thrombectomy showed good registration accuracy with a dice score higher than 92% and a mean landmark error lower than the quarter of a carotid diameter (8–10 mm).

Conclusion

A new registration method compatible with no contrast dye fluoroscopy has been proposed to guide the crossing from aortic arch to a carotid in mechanical thrombectomy. First evaluation showed the feasibility and accuracy of the method as well as its compatibility with clinical routine practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code implemented for this study is not publicly available.

References

  1. Friedrich B, Boeckh-Behrens T, Krüssmann V, Mönch S, Kirschke J, Kreiser K, Berndt M, Lehm M, Wunderlich S, Zimmer C, Kaesmacher J, Maegerlein C (2021) A short history of thrombectomy—Procedure and success analysis of different endovascular stroke treatment techniques. Interv Neuroradiol 27:249–256

    Article  CAS  PubMed  Google Scholar 

  2. Heider DM, Simgen A, Wagenpfeil G, Dietrich P, Yilmaz U, Mühl-Benninghaus R, Roumia S, Faßbender K, Reith W, Kettner M (2020) Why we fail: mechanisms and co-factors of unsuccessful thrombectomy in acute ischemic stroke. Neurol Sci 41:1547–1555

    Article  PubMed  PubMed Central  Google Scholar 

  3. Feddal A, Escalard S, Delvoye F, Fahed R, Desilles JP, Zuber K, Redjem H, Savatovsky JS, Ciccio G, Smajda S, Ben Maacha M, Mazighi M, Piotin M, Blanc R (2020) Fusion image guidance for supra-aortic vessel catheterization in neurointerventions: a feasibility study. AJNR Am J Neuroradiol 41(9):1663–1669

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16:642–661

    Article  CAS  PubMed  Google Scholar 

  5. Ambrosini P, Ruijters D, Niessen WJ, Moelker A, van Walsum T (2015) Continuous roadmapping in liver TACE procedures using 2D–3D catheter-based registration. Int J CARS 10:1357–1370

    Article  Google Scholar 

  6. Duménil A, Kaladji A, Castro M, Göksu C, Lucas A, Haigron P (2016) A versatile intensity-based 3D/2D rigid registration compatible with mobile C-arm for endovascular treatment of abdominal aortic aneurysm. Int J CARS 11:1713–1729

    Article  Google Scholar 

  7. Suh G-Y, Beygui RE, Fleischmann D, Cheng CP (2014) Aortic arch vessel geometries and deformations in patients with thoracic aortic aneurysms and dissections. J Vasc Interv Radiol 25:1903–1911

    Article  PubMed  Google Scholar 

  8. Zubair MM, de Beaufort HWL, Belvroy VM, Schwein A, Irshad A, Mohamed A, Gomez LF, Chinnadurai P, Nabi F, Yang EY, Trimarchi S, Reardon MJ, Bismuth J (2020) Impact of cardiac cycle on thoracic aortic geometry—morphometric analysis of ecg gated computed tomography. Ann Vasc Surg 65:174–182

    Article  PubMed  Google Scholar 

  9. Gherardini M, Mazomenos E, Menciassi A, Stoyanov D (2020) Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets. Comput Methods Programs Biomed 192:105420

    Article  PubMed  PubMed Central  Google Scholar 

  10. Varnavas A, Carrell T, Penney G (2013) Increasing the automation of a 2D–3D registration system. IEEE Trans Med Imaging 32:387–399

    Article  PubMed  Google Scholar 

  11. de Turenne A, Eugène F, Blanc R, Szewczyk J, Haigron P (2022) Cascaded U-net for segmentation of endovascular paths in mechanical thrombectomy. In: Linte CA, Siewerdsen JH (eds) Medical imaging 2022: image-guided procedures, robotic interventions, and modeling. SPIE, San Diego, p 56

    Google Scholar 

  12. Fantazzini A, Esposito M, Finotello A, Auricchio F, Pane B, Basso C, Spinella G, Conti M (2020) 3D Automatic segmentation of aortic computed tomography angiography combining multi-view 2D convolutional neural networks. Cardiovasc Eng Tech 11:576–586

    Article  Google Scholar 

  13. Caradu C, Stenson K, Houmaïda H, Le Ny J, Lalys F, Ducasse E, Gheysens B (2022) EndoNaut two-dimensional fusion imaging with a mobile C-arm for endovascular treatment of occlusive peripheral arterial disease. J Vasc Surg 75:651-659.e1

    Article  PubMed  Google Scholar 

  14. Heugen R (2015) VesselNavigator by philips. Eur Heart J 36:2483

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the French National Research Agency (ANR) through DEEP project (grant no. ANR-18-CE19-0027-01) and in the framework of the Investissement d’Avenir Program through Labex CAMI (ANR-11-LABX-0004).

Funding

This work was partially supported by the French National Research Agency (ANR) through DEEP project (Grant Num. ANR-18-CE19-0027-01) and in the framework of the Investissement d’Avenir Program through Labex CAMI (ANR-11-LABX-0004).

Author information

Authors and Affiliations

  1. CHU Rennes, Inserm, LTSI – UMR 1099, Univ Rennes, Rennes, France

    Aurélien de Turenne, François Eugène & Pascal Haigron

  2. Department of Interventional Neuroradiology, Hôpital de la Fondation Ophtalmologique Adolphe de Rothschild, 75019, Paris, France

    Raphaël Blanc

  3. Institut des Systèmes Intelligents et de Robotique, Sorbonne Universités, 75005, Paris, France

    Jérôme Szewczyk

Authors
  1. Aurélien de Turenne
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. François Eugène
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raphaël Blanc
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jérôme Szewczyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pascal Haigron
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AT and PH conceived the presented idea. AT and FE collected the data and carried out the experiments. AT performed the analysis. PH and JS verified the analytical method. PH supervised the findings of the work. AT wrote the manuscript with support from PH, FE, RB and JS. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Aurélien de Turenne.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This was an observational study for which anonymous retrospective data were used. It was performed in accordance with the ethical standards.

Consent to participate

Informed consent was obtained from all individual patients included in the study.

Consent for publication

Informed consent was obtained from all patients for whom data are included in this article.

Additional information

Publisher's Note

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

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

de Turenne, A., Eugène, F., Blanc, R. et al. Catheter navigation support for mechanical thrombectomy guidance: 3D/2D multimodal catheter-based registration with no contrast dye fluoroscopy. Int J CARS 19, 459–468 (2024). https://doi.org/10.1007/s11548-023-03034-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-023-03034-6

Keywords

Search

Navigation