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Magia: Robust automated image processing and kinetic modeling toolbox for PET neuroinformatics

View ORCID ProfileTomi Karjalainen, Jouni Tuisku, Severi Santavirta, View ORCID ProfileTatu Kantonen, Lauri Tuominen, Jussi Hirvonen, Jarmo Hietala, Juha O. Rinne, View ORCID ProfileLauri Nummenmaa
doi: https://doi.org/10.1101/604835
Tomi Karjalainen
1Turku PET Centre, Turku University Hospital, Turku, Finland
2Turku PET Centre, University of Turku, Turku, Finland
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  • For correspondence: tomi.karjalainen@utu.fi
Jouni Tuisku
2Turku PET Centre, University of Turku, Turku, Finland
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Severi Santavirta
2Turku PET Centre, University of Turku, Turku, Finland
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Tatu Kantonen
2Turku PET Centre, University of Turku, Turku, Finland
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Lauri Tuominen
3The Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
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Jussi Hirvonen
2Turku PET Centre, University of Turku, Turku, Finland
4Department of Radiology, University of Turku, Turku, Finland
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Jarmo Hietala
2Turku PET Centre, University of Turku, Turku, Finland
5Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
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Juha O. Rinne
2Turku PET Centre, University of Turku, Turku, Finland
6Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
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Lauri Nummenmaa
2Turku PET Centre, University of Turku, Turku, Finland
7Department of Psychology, University of Turku, Turku, Finland
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Abstract

Introduction Modelling of the radioactivity images produced by PET scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images. The process is challenging to automatize mainly because of manual work related to input generation, thus prohibiting large-scale standardized analysis of brain PET data. To resolve this problem, we introduce the Magia pipeline that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia in the automatic brain-PET data processing with four tracers binding to different binding sites: [11C]raclopride, [11C]carfentanil, [11C]MADAM, and [11C]PiB.

Materials and methods For each tracer, we processed 30 historical control subjects’ data with manual and automated methods. Five persons manually delineated the reference regions (cerebellar or occipital cortex depending on tracer) for each subject according to written and visual instructions. The automatic reference-region extraction was based on FreeSurfer parcellations. We first assessed inter-operator variance resulting from manual delineation of reference regions. Then we compared the differences between the manually and automatically produced reference regions and the subsequently obtained metrics.

Results The manually delineated reference regions were remarkably different from each other. The differences translated into differences in outcome measures (binding potential or SUV-ratio), and the intra-class correlation coefficients were between 47 % and 96 % for the tracers. While the Magia-derived reference regions were topographically very different from the manually defined reference regions, Magia produced outcome measures highly consistent with average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3-5 % higher binding potentials as a result of slightly lower time-integrals of reference region time-activity curves.

Conclusion Even if Magia produces reference regions that are anatomically different from manually drawn reference regions, the resulting outcome measures are highly similar. Based on these results and considering the high inter-operator variance of the manual method, the high level of standardization and strong scalability of Magia, we conclude that Magia can be reliably used to process brain PET data.

Footnotes

  • Competing Interests’ Statement: None

  • Added analyses regarding operator-dependency

  • https://github.com/tkkarjal/magia

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 15, 2019.
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Magia: Robust automated image processing and kinetic modeling toolbox for PET neuroinformatics
Tomi Karjalainen, Jouni Tuisku, Severi Santavirta, Tatu Kantonen, Lauri Tuominen, Jussi Hirvonen, Jarmo Hietala, Juha O. Rinne, Lauri Nummenmaa
bioRxiv 604835; doi: https://doi.org/10.1101/604835
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Magia: Robust automated image processing and kinetic modeling toolbox for PET neuroinformatics
Tomi Karjalainen, Jouni Tuisku, Severi Santavirta, Tatu Kantonen, Lauri Tuominen, Jussi Hirvonen, Jarmo Hietala, Juha O. Rinne, Lauri Nummenmaa
bioRxiv 604835; doi: https://doi.org/10.1101/604835

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