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An Evaluation of Adaptive Biomechanical Non-Rigid Registration for Brain Glioma Resection Using Image-Guided Neurosurgery

  • Fotis Drakopoulos
  • Chengjun Yao
  • Yixun Liu
  • Nikos ChrisochoidesEmail author
Conference paper

Abstract

Interventional MRI (iMRI) has proven to be an effective tool for the visualization of brain deformation and for the optimization of the maximal safe volumetric tumor resection during Image-Guided Neurosurgery. Earlier we proposed two adaptive non-rigid registration methods between pre-operative and intra-operative MRI based on: (i) Nested Expectation Maximization (NEM) [8, 20] which implicitly compensates for tissue removal, and (ii) Geometric-based which explicitly adapts the mesh to compensate for the changes in the geometry of the brain [4, 21]. In this paper, we assess the accuracy of these methods and compare them with two widely used registration schemes: ITK’s rigid registration and ITK’s Physics-Based Non-Rigid Registration (PBNRR). The evaluation is based on registration error, for the brain deformation induced by cerebral glioma resection, and it utilizes three metrics for the error: (i) 100% Hausdorff Distance, (ii) error at specific anatomical points identified by a neurosurgeon, and (iii) visual inspection by a neurosurgeon. We conduct a retrospective study on ten patients with a malignant glioma. The evaluation shows that the geometric adaptive approach achieves the most accurate alignments compared to ITK’s PBNRR and the Nested Expectation Maximization. It significantly reduces the alignment error due to rigid registration commonly used by commercial neuronavigators, and completes a volumetric alignment, on average, in about 2.3 min on a 12-core Linux workstation, satisfying the time constraints imposed by neurosurgery.

Keywords

Hausdorff Distance Registration Accuracy Rigid Registration Soft Tissue Deformation Brain Deformation 
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.

Notes

Acknowledgments

Research reported in this publication was supported in part by the Modeling and Simulation Fellowship at Old Dominion University, CCF-1439079, John Simon Guggenheim Foundation, and by the Richard T. Cheng Endowment.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fotis Drakopoulos
    • 1
  • Chengjun Yao
    • 2
  • Yixun Liu
    • 1
  • Nikos Chrisochoides
    • 1
    Email author
  1. 1.The Department of Computer ScienceOld Dominion UniversityNorfolkUSA
  2. 2.The Department of NeurosurgeryHuashan HospitalShanghaiChina

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