An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projecti...

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Bibliographic Details
Published in:Frontiers in neuroinformatics 2018-12, Vol.12, p.94-94
Main Authors: Marcoux, Arnaud, Burgos, Ninon, Bertrand, Anne, Teichmann, Marc, Routier, Alexandre, Wen, Junhao, Samper-González, Jorge, Bottani, Simona, Durrleman, Stanley, Habert, Marie-Odile, Colliot, Olivier
Format: Article
Language:English
Subjects:
Alzheimer's disease
Aphasia
Artificial intelligence
Automation
brain
Brain research
Cognitive science
Computer Science
Cortex
Dementia
Dementia disorders
Machine learning
Magnetic resonance imaging
Medical Imaging
Metabolism
Nervous system
neurodegenerative diseases
Neuroscience
NMR
Nuclear magnetic resonance
Performance evaluation
PET
Positron emission tomography
Statistical analysis
surface analysis
workflow
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Summary:We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run.
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2018.00094