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Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems.

Fotis Drakopoulos1, Christos Tsolakis1,2, Angelos Angelopoulos1,2

  • 1Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.

Frontiers in Digital Health
|October 29, 2021
PubMed
Summary

A new Adaptive Physics-Based Non-Rigid Registration (A-PBNRR) method significantly improves brain tumor resection accuracy by modeling intraoperative deformation. This advanced technique offers faster, more precise image guidance for neurosurgery compared to existing methods.

Keywords:
deep learningdeformable registrationmachine learningmedical image computingmesh generationmixed realityneuronavigation systemsneurosurgery

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Image Registration
  • Computational Anatomy

Background:

  • Image-guided neurosurgery relies on co-registered preoperative imaging (anatomical, functional, diffusion tensor imaging) for safe tumor resection in eloquent brain areas.
  • Brain deformation during surgery, especially after tumor resection, challenges the accuracy of preoperative image guidance.
  • Non-Rigid Registration (NRR) aims to update preoperative images to match intraoperative anatomy while preserving image quality.

Purpose of the Study:

  • To compare the accuracy and performance of several non-rigid registration methods for handling brain deformation during neurosurgery.
  • To introduce a novel adaptive method (A-PBNRR) that automatically handles deformation caused by tumor resection.
  • To present a new mixed reality system integrating ultrasound, MRI, and CT for improved user experience in image-guided surgery.

Main Methods:

  • The study involved 30 glioma surgeries with significant tumor resection volumes.
  • An Adaptive Physics-Based Non-Rigid Registration (A-PBNRR) method was developed and applied to register preoperative and intraoperative MRI.
  • A-PBNRR was compared against rigid registration, B-Spline non-rigid registration (both in 3D Slicer), and traditional Physics-Based Non-Rigid Registration (PBNRR in ITK) using visual assessment, Hausdorff Distance, and landmark-based metrics.

Main Results:

  • A-PBNRR demonstrated over fivefold improvement in registration accuracy compared to rigid and traditional PBNRR, and fourfold improvement over B-Spline methods.
  • The A-PBNRR method achieved an average application time of under 2 minutes, indicating clinical feasibility.
  • Performance analysis confirmed A-PBNRR's ability to effectively model deformation, including that caused by resection.

Conclusions:

  • The A-PBNRR method significantly outperforms existing registration techniques in modeling intraoperative brain deformation, particularly after resection.
  • Both the accuracy and speed of A-PBNRR are sufficient for clinical application in the operating room.
  • A-PBNRR, combined with a mixed reality system, offers a powerful, cost-effective alternative to current neuronavigation systems.