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Learning Expected Appearances for Intraoperative Registration during Neurosurgery.

Nazim Haouchine1, Reuben Dorent1, Parikshit Juvekar1

  • 1Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 19, 2024
PubMed
Summary

This study introduces a new method for intraoperative patient-to-image registration using learned Expected Appearances. This approach enhances neuronavigation accuracy by reducing reliance on low-quality intraoperative images.

Keywords:
3D Pose EstimationAugmented RealityImage-guided NeurosurgeryIntraoperative RegistrationNeural Image Analogy

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Area of Science:

  • Medical Imaging
  • Computer-Aided Surgery
  • Surgical Navigation

Background:

  • Intraoperative patient-to-image registration is crucial for accurate surgical navigation.
  • Conventional methods struggle with low-resolution, distorted, and noisy intraoperative images, impacting accuracy.
  • Existing neuronavigation systems require robust registration for effective guidance during brain surgery.

Purpose of the Study:

  • To develop a novel method for intraoperative patient-to-image registration by learning Expected Appearances.
  • To improve the accuracy and reliability of neuronavigation systems in brain surgery.
  • To mitigate the challenges posed by suboptimal intraoperative imaging conditions.

Main Methods:

  • Synthesizing patient-specific expected views from preoperative imaging through a surgical microscope.
  • Estimating camera pose by minimizing dissimilarity between intraoperative 2D views and synthesized expected textures.
  • Transferring computational load to the preoperative stage to overcome intraoperative image quality limitations.

Main Results:

  • The proposed method demonstrated superior performance compared to state-of-the-art techniques.
  • Achieved registration accuracies meeting current clinical standards in neuronavigation.
  • Successfully evaluated on both synthetic and retrospective clinical data from six cases.

Conclusions:

  • The Expected Appearances method offers a robust solution for intraoperative registration.
  • This approach significantly enhances neuronavigation accuracy, especially under challenging intraoperative conditions.
  • The method shows promise for widespread adoption in image-guided neurosurgery.