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Model-Based Identification of Anatomical Boundary Conditions in Living Tissues

  • Igor Peterlik
  • Hadrien Courtecuisse
  • Christian Duriez
  • Stéphane Cotin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8498)

Abstract

In this paper, we present a novel method dealing with the identification of boundary conditions of a deformable organ, a particularly important step for the creation of patient-specific biomechanical models of the anatomy. As an input, the method requires a set of scans acquired in different body positions. Using constraint-based finite element simulation, the method registers the two data sets by solving an optimization problem minimizing the energy of the deformable body while satisfying the constraints located on the surface of the registered organ. Once the equilibrium of the simulation is attained (i.e. the organ registration is computed), the surface forces needed to satisfy the constraints provide a reliable estimation of location, direction and magnitude of boundary conditions applied to the object in the deformed position. The method is evaluated on two abdominal CT scans of a pig acquired in flank and supine positions. We demonstrate that while computing a physically admissible registration of the liver, the resulting constraint forces applied to the surface of the liver strongly correlate with the location of the anatomical boundary conditions (such as contacts with bones and other organs) that are visually identified in the CT images.

Keywords

Iterative Close Point Surface Load Constraint Force Iterative Close Point Deformable Image Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Igor Peterlik
    • 1
    • 2
    • 4
  • Hadrien Courtecuisse
    • 1
    • 2
    • 3
  • Christian Duriez
    • 2
  • Stéphane Cotin
    • 1
    • 2
  1. 1.Institut Hospitalo-UniversitaireStrasbourgFrance
  2. 2.SHACRA TeamInriaFrance
  3. 3.AVR TeamCNRSFrance
  4. 4.Institute of Computer ScienceMasaryk UniversityCzech Republic

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