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Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma

Authors
  • Visser, Martin1
  • Petr, Jan2
  • Müller, Domenique M. J.3
  • Eijgelaar, Roelant S.3
  • Hendriks, Eef J.1
  • Witte, Marnix4
  • Barkhof, Frederik1, 5, 6
  • van Herk, Marcel7
  • Mutsaerts, Henk J. M. M.1
  • Vrenken, Hugo1
  • de Munck, Jan C.1
  • De Witt Hamer, Philip C.3
  • 1 Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam , (Netherlands)
  • 2 Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden , (Germany)
  • 3 Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, Amsterdam , (Netherlands)
  • 4 Department of Radiotherapy, Netherlands Cancer Institute, Amsterdam , (Netherlands)
  • 5 UCL Queen Square Institute of Neurology, University College London, London , (United Kingdom)
  • 6 UCL Institute of Healthcare Engineering, University College London, London , (United Kingdom)
  • 7 Division of Cancer Sciences, Manchester Cancer Research Centre, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester , (United Kingdom)
Type
Published Article
Journal
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Publication Date
Jun 05, 2020
Volume
14
Identifiers
DOI: 10.3389/fnins.2020.00585
PMID: 32581699
PMCID: PMC7290158
Source
PubMed Central
Keywords
License
Unknown

Abstract

To summarize the distribution of glioma location within a patient population, registration of individual MR images to anatomical reference space is required. In this study, we quantified the accuracy of MR image registration to anatomical reference space with linear and non-linear transformations using estimated tumor targets of glioblastoma and lower-grade glioma, and anatomical landmarks at pre- and post-operative time-points using six commonly used registration packages (FSL, SPM5, DARTEL, ANTs, Elastix, and NiftyReg). Routine clinical pre- and post-operative, post-contrast T1-weighted images of 20 patients with glioblastoma and 20 with lower-grade glioma were collected. The 2009a Montreal Neurological Institute brain template was used as anatomical reference space. Tumors were manually segmented in the patient space and corresponding healthy tissue was delineated as a target volume in the anatomical reference space. Accuracy of the tumor alignment was quantified using the Dice score and the Hausdorff distance. To measure the accuracy of general brain alignment, anatomical landmarks were placed in patient and in anatomical reference space, and the landmark distance after registration was quantified. Lower-grade gliomas were registered more accurately than glioblastoma. Registration accuracy for pre- and post-operative MR images did not differ. SPM5 and DARTEL registered tumors most accurate, and FSL least accurate. Non-linear transformations resulted in more accurate general brain alignment than linear transformations, but tumor alignment was similar between linear and non-linear transformation. We conclude that linear transformation suffices to summarize glioma locations in anatomical reference space.

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