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Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements.

David M Cash1, Michael I Miga, Tuhin K Sinha

  • 1Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235 USA.

IEEE Transactions on Medical Imaging
|November 11, 2005
PubMed
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This study introduces a new method for image-guided liver surgery, improving accuracy by identifying and aligning minimally deformed tissue regions. The combined approach accurately located subsurface targets within 4 mm in phantom experiments.

Area of Science:

  • Medical imaging
  • Surgical navigation
  • Computational anatomy

Background:

  • Accurate image-guided liver surgery necessitates accounting for soft tissue deformation.
  • Preoperative 3D surfaces and intraoperative range scans represent different states of the liver.
  • Traditional rigid registration methods may not adequately handle tissue deformation.

Purpose of the Study:

  • To develop and evaluate a novel deformation-identifying rigid registration (DIRR) method for liver surgery.
  • To integrate DIRR with a finite element model (FEM) for comprehensive deformation compensation.
  • To assess the accuracy of the DIRR and DIRR/FEM approaches in phantom experiments.

Main Methods:

  • Implemented a DIRR technique using a modified closest point distance cost function to align minimally deformed regions.

Related Experiment Videos

  • Employed a linearly elastic FEM with an incremental framework to resolve geometric nonlinearities.
  • Utilized intraoperative range scan surfaces as boundary conditions for the FEM.

    • Main Results:

    • The DIRR approach successfully identified deforming regions in 90% of realistic surgical exposure scenarios.
    • The combined DIRR/FEM algorithm achieved subsurface target localization accuracy within 4 mm in phantom studies.
    • Demonstrated the fidelity of both DIRR and DIRR/FEM methods in phantom experiments.

    Conclusions:

    • The DIRR method provides robust rigid alignment by focusing on minimally deformed areas.
    • The integration of DIRR with FEM effectively compensates for liver tissue deformation during surgery.
    • This combined approach shows significant potential for enhancing the precision of image-guided liver surgery.