Anatomical deformation prediction using artificial intelligence for fusion imaging during aortic endovascular…

Abstract

Placement of an aortic stent graft induces anatomical deformations, particularly at the iliac level, because of the introduction of rigid devices. Anticipating these changes may improve the accuracy of fusion guidance during endovascular procedures. In this prospective single-center study, a deep learning–based sizing software was used to generate a deformation-adjusted three-dimensional fusion mask, which was compared with a conventional, nondeformed mask using intraoperative angiography as the reference standard. In a cohort of 31 patients, the deformation–adjusted mask significantly improved the fusion accuracy for renal artery ostia positioning, iliac artery alignment, and guidewire localization, without any additional acquisition time. These findings suggest that deformation-aware fusion masks may enhance intraoperative navigation during aortic endovascular repair.

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BibTeX

@article{vannier2026anatomical,
  title = {{Anatomical deformation prediction using artificial intelligence for fusion imaging during aortic endovascular procedures}},
  author = {Cindy Vannier and Florent Lalys and Jade Hollier-Ben Turkia and Antoine Lucas and Adrien Kaladji},
  journal = {Journal of Vascular Surgery Cases and Innovative Techniques},
  year = {2026},
  month = oct,
  doi = {10.1016/j.jvscit.2026.102297},
  url = {https://hal.science/hal-05646654v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Anatomical deformation prediction using artificial intelligence for fusion imaging during aortic endovascular procedures
AU  - Cindy Vannier
AU  - Florent Lalys
AU  - Jade Hollier-Ben Turkia
AU  - Antoine Lucas
AU  - Adrien Kaladji
PY  - 2026
DA  - 2026/10/13
JO  - Journal of Vascular Surgery Cases and Innovative Techniques
DO  - 10.1016/j.jvscit.2026.102297
UR  - https://hal.science/hal-05646654v1
AB  - Placement of an aortic stent graft induces anatomical deformations, particularly at the iliac level, because of the introduction of rigid devices. Anticipating these changes may improve the accuracy of fusion guidance during endovascular procedures. In this prospective single-center study, a deep learning–based sizing software was used to generate a deformation-adjusted three-dimensional fusion mask, which was compared with a conventional, nondeformed mask using intraoperative angiography as the reference standard. In a cohort of 31 patients, the deformation–adjusted mask significantly improved the fusion accuracy for renal artery ostia positioning, iliac artery alignment, and guidewire localization, without any additional acquisition time. These findings suggest that deformation-aware fusion masks may enhance intraoperative navigation during aortic endovascular repair.
SN  - 2468-4287
ER  -

Abstract

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