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Rigshospitalet - a part of Copenhagen University Hospital
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Automatic delineation of brain regions on MRI and PET images from the pig

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BACKGROUND: The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated.

COMPARISON WITH EXISTING METHODS: Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake.

NEW METHOD: A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer.

RESULTS: MRI and [11C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [11C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames.

CONCLUSION: We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration.

Original languageEnglish
JournalJournal of Neuroscience Methods
Volume294
Pages (from-to)51-58
Number of pages8
ISSN0165-0270
DOIs
Publication statusPublished - 15 Jan 2018

    Research areas

  • Journal Article

ID: 52582978