Using Curve-Fitting of Curvilinear Features for Assessing Registration of Clinical Neuropathology with in Vivo MRI

  • Authors:
  • Philippe Laissue;Chris Kenwright;Ali Hojjat;Alan Colchester

  • Affiliations:
  • Medical Image Computing, University of Kent, Canterbury, United Kingdom CT2 7PD;Medical Image Computing, University of Kent, Canterbury, United Kingdom CT2 7PD;Medical Image Computing, University of Kent, Canterbury, United Kingdom CT2 7PD;Medical Image Computing, University of Kent, Canterbury, United Kingdom CT2 7PD

  • Venue:
  • MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
  • Year:
  • 2008

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Abstract

Traditional neuropathological examination provides information about neurological disease or injury of a patient at a high-resolution level. Correlating this type of post mortemdiagnosis with in vivoimage data of the same patient acquired by non-invasive tomographic scans greatly complements the interpretation of any disease or injury. We present the validation of a registration method for correlating macroscopic pathological images with MR images of the same patient. This also allows for 3-D mapping of the distribution of pathological changes throughout the brain. As the validation deals with datasets of widely differing sampling, we propose a method using smooth curvilinear anatomical features in the brain which allows interpolation between wide-spaced samples. Curvilinear features are common anatomically, and if selected carefully have the potential to allow determination of the accuracy of co-registration across large areas of a volume of interest.