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Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12904)

Abstract

Patient-specific Biomechanical Models (PBMs) can enhance computer assisted surgical procedures with critical information. Although pre-operative data allow to parametrize such PBMs based on each patient’s properties, they are not able to fully characterize them. In particular, simulation boundary conditions cannot be determined from pre-operative modalities, but their correct definition is essential to improve the PBM predictive capability. In this work, we introduce a pipeline that provides an up-to-date estimate of boundary conditions, starting from the pre-operative model of patient anatomy and the displacement undergone by points visible from an intra-operative vision sensor. The presented pipeline is experimentally validated in realistic conditions on an ex vivo pararenal fat tissue manipulation. We demonstrate its capability to update a PBM reaching clinically acceptable performances, both in terms of accuracy and intra-operative time constraints.

Keywords

  • Intra-operative model update
  • Boundary conditions
  • Biomechanical modeling

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Notes

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    Project available at https://gitlab.com/altairLab/banet.

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Acknowledgements

Authors would like to thank the preclinical research staff at IHU Strasbourg for their assistance and support during the experiments. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 742671 “ARS”), from French state funds managed within the “Plan Investissements d’Avenir” and from the ANR (reference ANR-10-IAHU-02).

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Correspondence to Eleonora Tagliabue .

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Tagliabue, E. et al. (2021). Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12904. Springer, Cham. https://doi.org/10.1007/978-3-030-87202-1_36

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  • DOI: https://doi.org/10.1007/978-3-030-87202-1_36

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