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Related Experiment Video

Updated: Dec 17, 2025

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Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma.

Martin Visser1, Jan Petr2, Domenique M J Müller3

  • 1Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands.

Frontiers in Neuroscience
|June 26, 2020
PubMed
Summary

Linear and non-linear transformations accurately register glioma locations in anatomical space. Lower-grade gliomas show better registration than glioblastomas, with similar accuracy for pre- and post-operative scans. Linear transformations are sufficient for summarizing glioma distribution.

Keywords:
computer-assistedgliomaimage processinglinear registrationmagnetic resonance imagingnon-linear registration

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Radiology

Background:

  • Accurate glioma location summarization requires registering patient MR images to a standard anatomical space.
  • Evaluating different registration methods is crucial for reliable glioma distribution analysis.

Purpose of the Study:

  • To quantify the accuracy of MR image registration for gliomas using linear and non-linear transformations.
  • To compare the performance of six common registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, NiftyReg).

Main Methods:

  • Collected pre- and post-operative T1-weighted MR images from 40 patients (20 glioblastoma, 20 lower-grade glioma).
  • Used the Montreal Neurological Institute (MNI) brain template as the anatomical reference space.
  • Quantified registration accuracy using Dice score, Hausdorff distance for tumors, and landmark distance for general brain alignment.

Main Results:

  • Lower-grade gliomas registered more accurately than glioblastomas.
  • Registration accuracy was consistent between pre- and post-operative images.
  • SPM5 and DARTEL showed the highest tumor registration accuracy; FSL showed the least.
  • Non-linear transformations improved general brain alignment but not tumor alignment compared to linear transformations.

Conclusions:

  • Linear transformations are adequate for summarizing glioma locations in anatomical reference space.
  • The choice of registration package significantly impacts accuracy, with SPM5 and DARTEL being superior for tumor registration.