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Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair

  • Katharina BreiningerEmail author
  • Moritz Hanika
  • Mareike Weule
  • Markus Kowarschik
  • Marcus Pfister
  • Andreas Maier
Original Article
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Abstract

Purpose

Endovascular repair of aortic aneurysms (EVAR) can be supported by fusing pre- and intraoperative data to allow for improved navigation and to reduce the amount of contrast agent needed during the intervention. However, stiff wires and delivery devices can deform the vasculature severely, which reduces the accuracy of the fusion. Knowledge about the 3D position of the inserted instruments can help to transfer these deformations to the preoperative information.

Method

We propose a method to simultaneously reconstruct the stiff wires in both iliac arteries based on only a single monoplane acquisition, thereby avoiding interference with the clinical workflow. In the available X-ray projection, the 2D course of the wire is extracted. Then, a virtual second view of each wire orthogonal to the real projection is estimated using the preoperative vessel anatomy from a computed tomography angiography as prior information. Based on the real and virtual 2D wire courses, the wires can then be reconstructed in 3D using epipolar geometry.

Results

We achieve a mean modified Hausdorff distance of 4.2 mm between the estimated 3D position and the true wire course for the contralateral side and 4.5 mm for the ipsilateral side.

Conclusion

The accuracy and speed of the proposed method allow for use in an intraoperative setting of deformation correction for EVAR.

Keywords

EVAR Fluoroscopy Guide wire reconstruction Image guidance 
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Notes

Acknowledgements

We thank Dr. Giasemi Koutouzi and Dr. Mårten Falkenberg from Sahlgrenska University Hospital, Gothenburg, Sweden, for providing the data and the registration of intraoperative and preoperative scans.

Compliance with ethical standards

Conflict of interest

A. Maier has no conflict of interest to declare. K. Breininger is funded by Siemens Healthcare GmbH. M. Weule and M. Hanika were working students employed by Siemens Healthcare GmbH at the time of this study. M. Pfister and M. Kowarschik are employees of Siemens Healthcare GmbH.

Ethical approval

This study has been performed retrospectively. For this type of study formal consent is not required.

Informed consent

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

References

  1. 1.
    Abi-Jaoudeh N, Kruecker J, Kadoury S, Kobeiter H, Venkatesan AM, Levy E, Wood BJ (2012) Multimodality image fusion-guided procedures: technique, accuracy, and applications. Cardiovasc Interv Radiol 35(5):986–998.  https://doi.org/10.1007/s00270-012-0446-5 CrossRefGoogle Scholar
  2. 2.
    Ambrosini P, Ruijters D, Niessen WJ, Moelker A, van Walsum T (2017) Fully automatic and real-time catheter segmentation in X-ray fluoroscopy. In: MICCAI 2017: 20th international conference, proceedings, Part II, pp 577–585Google Scholar
  3. 3.
    Baert SAM, van de Kraats EB, van Walsum T, Viergever MA, Niessen WJ (2003) Three-dimensional guide-wire reconstruction from biplane image sequences for integrated display in 3-D vasculature. IEEE Trans Med Imaging 22(10):1252–1258.  https://doi.org/10.1109/TMI.2003.817791 CrossRefPubMedGoogle Scholar
  4. 4.
    Bender HJ, Männer R, Poliwoda C, Roth S, Walz M (1999) Reconstruction of 3D catheter paths from 2D X-ray projections. In: Taylor C, Colchester A (eds) Medical image computing and computer-assisted intervention—MICCAI’99. Springer, Berlin, pp 981–989CrossRefGoogle Scholar
  5. 5.
    Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, BerlinGoogle Scholar
  6. 6.
    Breininger K, Albarqouni S, Kurzendorfer T, Pfister M, Kowarschik M, Maier A (2018) Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair. Int J Comput Assist Radiol Sur.  https://doi.org/10.1007/s11548-018-1779-6 CrossRefGoogle Scholar
  7. 7.
    Breininger K, Hanika M, Weule M, Kowarschik M, Pfister M, Maier A (2019) 3D-reconstruction of stiff wires from a single monoplane X-ray image. In: Bildverarbeitung für die Medizin (BVM) WorkshopGoogle Scholar
  8. 8.
    Breininger K, Pfister M, Koutouzi G, Kowarschik M, Maier A (2017) Estimation of femoral artery access location for anatomic deformation correction. In: Skalej M, Hoeschen C (eds.) 3rd conference on image-guided interventions & Fokus Neuroradiologie, pp 23–24Google Scholar
  9. 9.
    Breininger K, Würfl T, Kurzendorfer T, Albarqouni S, Pfister M, Kowarschik M, Navab N, Maier A (2018) Multiple device segmentation for fluoroscopic imaging using multi-task learning. Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis. Springer, Cham, pp 19–27CrossRefGoogle Scholar
  10. 10.
    Brückner M, Deinzer F, Denzler J (2009) Temporal estimation of the 3d guide-wire position using 2d X-ray images. In: Yang GZ, Hawkes D, Rueckert D, Noble A, Taylor C (eds) Medical image computing and computer-assisted intervention—MICCAI 2009. Springer, Berlin, pp 386–393CrossRefGoogle Scholar
  11. 11.
    Dijkstra ML, Eagleton MJ, Greenberg RK, Mastracci T, Hernandez A (2011) Intraoperative C-arm cone-beam computed tomography in fenestrated/branched aortic endografting. J Vasc Surg 53(3):583–590.  https://doi.org/10.1016/j.jvs.2010.09.039 CrossRefPubMedGoogle Scholar
  12. 12.
    Dubuisson M, Jain AK (1994) A modified Hausdorff distance for object matching. In: Proceedings of 12th international conference on pattern recognition 1:566–568.  https://doi.org/10.1109/ICPR.1994.576361
  13. 13.
    Gindre J, Bel-Brunon A, Rochette M, Lucas A, Kaladji A, Haigron P, Combescure A (2017) Patient-specific finite-element simulation of the insertion of guidewire during an EVAR procedure: guidewire position prediction validation on 28 cases. IEEE Trans Biomed Eng 64(5):1057–1066.  https://doi.org/10.1109/TBME.2016.2587362 CrossRefPubMedGoogle Scholar
  14. 14.
    Glöckler M, Halbfaß J, Koch A, Achenbach S, Dittrich S (2013) Multimodality 3D-roadmap for cardiovascular interventions in congenital heart disease—a single-center, retrospective analysis of 78 cases. Catheter Cardiovasc Interv 82(3):436–442.  https://doi.org/10.1002/ccd.24646 CrossRefPubMedGoogle Scholar
  15. 15.
    Hoffmann M, Brost A, Jakob C, Bourier F, Koch M, Kurzidim K, Hornegger J, Strobel N (2012) Semi-automatic catheter reconstruction from two views. In: Proceedings of the 15th international conference on medical image computing and computer-assisted intervention - Part II, pp 584–591 .  https://doi.org/10.1007/978-3-642-33418-4_72 CrossRefGoogle Scholar
  16. 16.
    Hoffmann M, Brost A, Jakob C, Koch M, Bourier F, Kurzidim K, Hornegger J, Strobel N (2013)Reconstruction method for curvilinear structures from two views. pp 86712F–86712F–8 .  https://doi.org/10.1117/12.2006346
  17. 17.
    Kaladji A, Dumenil A, Castro M, Cardon A, Becquemin JP, Bou-Saïd B, Lucas A, Haigron P (2013) Prediction of deformations during endovascular aortic aneurysm repair using finite element simulation. Comput Med Imaging Gr 37(2):142–149.  https://doi.org/10.1016/j.compmedimag.2013.03.002 Special Issue on Mixed Reality Guidance of Therapy—Towards Clinical ImplementationCrossRefGoogle Scholar
  18. 18.
    Kauffmann C, Douane F, Therasse E, Lessard S, Elkouri S, Gilbert P, Beaudoin N, Pfister M, Blair JF, Soulez G (2015) Source of errors and accuracy of a two-dimensional/three-dimensional fusion road map for endovascular aneurysm repair of abdominal aortic aneurysm. J Vasc Interv Radiol 26(4):544–551.  https://doi.org/10.1016/j.jvir.2014.12.019 CrossRefPubMedGoogle Scholar
  19. 19.
    Koutouzi G, Pfister M, Breininger K, Hellström M, Roos H, Falkenberg M (2019) Iliac artery deformation during EVAR. Vascular 1708538119840565.  https://doi.org/10.1177/1708538119840565 PMID: 30917751CrossRefGoogle Scholar
  20. 20.
    Lessard S, Kauffmann C, Pfister M, Cloutier G, Therasse E, de Guise JA, Soulez G (2015) Automatic detection of selective arterial devices for advanced visualization during abdominal aortic aneurysm endovascular repair. Med Eng Phys 37(10):979–986CrossRefGoogle Scholar
  21. 21.
    Mastmeyer A, Pernelle G, Barber, L, Pieper S, Fortmeier D, Wells S, Handels H, Kapur T (2017) Model-based catheter segmentation in MRI-images. In: MICCAI workshop on interactive medical image computing, IMIC 2015, 18th international conference on medical image computing and computer-assisted intervention—MICCAI 2015Google Scholar
  22. 22.
    Mastmeyer A, Pernelle G, Ma R, Barber L, Kapur T (2017) Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images. Med Image Anal 42:173–188.  https://doi.org/10.1016/j.media.2017.06.011 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Mehrtash A, Ghafoorian M, Pernelle G, Ziaei A, Heslinga FG, Tuncali K, Fedorov A, Kikinis R, Tempany CM, Wells WM, Abolmaesumi P, Kapur T (2019) Automatic needle segmentation and localization in MRI with 3-D convolutional neural networks: application to MRI-targeted prostate biopsy. IEEE Trans Med Imaging 38(4):1026–1036.  https://doi.org/10.1109/TMI.2018.2876796 CrossRefPubMedGoogle Scholar
  24. 24.
    Mohammadi H, Lessard S, Therasse E, Mongrain R, Soulez G (2018) A numerical preoperative planning model to predict arterial deformations in endovascular aortic aneurysm repair. Ann Biomed Eng 46(12):2148–2161.  https://doi.org/10.1007/s10439-018-2093-8 CrossRefPubMedGoogle Scholar
  25. 25.
    Panuccio G, Torsello GF, Pfister M, Bisdas T, Bosiers M, Torsello G, Austermann M (2016) Computer-aided endovascular aortic repair using fully automated two- and three-dimensional fusion imaging. J Vasc Surg 64:1587–1594CrossRefGoogle Scholar
  26. 26.
    Petković T, Homan R, Lončarić S (2014) Real-time 3D position reconstruction of guidewire for monoplane X-ray. Comput Med Imaging Gr 38(3):211–223.  https://doi.org/10.1016/j.compmedimag.2013.12.006 CrossRefGoogle Scholar
  27. 27.
    Pourtaherian A, Ghazvinian Zanjani F, Zinger S, Mihajlovic N, Ng GC, Korsten HHM, de With PHN (2018) Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks. Int J Comput Assist Radiol Surg 13(9):1321–1333.  https://doi.org/10.1007/s11548-018-1798-3 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Rossitti S, Pfister M (2009) 3D road-mapping in the endovascular treatment of cerebral aneurysms and arteriovenous malformations. Interv Neuroradiol 15(3):283–290.  https://doi.org/10.1177/159101990901500305 PMID: 20465911CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Schulz CJ, Schmitt M, Böckler D, Geisbüsch P (2016) Fusion imaging to support endovascular aneurysm repair using 3D–3D registration. J Endovasc Ther 23(5):791–799.  https://doi.org/10.1177/1526602816660327 PMID: 27456083CrossRefPubMedGoogle Scholar
  30. 30.
    Tacher V, Lin M, Desgranges P, Deux JF, Grünhagen T, Becquemin JP, Luciani A, Rahmouni A, Kobeiter H (2013) Image guidance for endovascular repair of complex aortic aneurysms: comparison of two-dimensional and three-dimensional angiography and image fusion. J Vasc Interv Radiol 24(11):1698–1706CrossRefGoogle Scholar
  31. 31.
    Toth D, Pfister M, Maier A, Kowarschik M, Hornegger J (2015) Adaption of 3D models to 2D X-ray images during endovascular abdominal aneurysm repair. In: MICCAI 2015: 18th international conference, proceedings, Part I, pp 339–346Google Scholar
  32. 32.
    van Walsum T, Baert SAM, Niessen WJ (2005) Guide wire reconstruction and visualization in 3DRA using monoplane fluoroscopic imaging. IEEE Trans Med Imaging 24(5):612–623.  https://doi.org/10.1109/TMI.2005.844073 CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  • Katharina Breininger
    • 1
    Email author
  • Moritz Hanika
    • 2
  • Mareike Weule
    • 2
  • Markus Kowarschik
    • 2
  • Marcus Pfister
    • 2
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  2. 2.Siemens Healthcare GmbHForchheimGermany

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