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dc.contributor.author
Ioanas, Horea-Ioan
dc.contributor.author
Marks, Markus
dc.contributor.author
Yanik, Mehmet Fatih
dc.contributor.author
Rudin, Markus
dc.date.accessioned
2020-01-28T11:50:58Z
dc.date.available
2020-01-27T15:07:10Z
dc.date.available
2020-01-28T11:50:58Z
dc.date.issued
2019-10-06
dc.identifier.other
10.1101/619650
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/394464
dc.identifier.doi
10.3929/ethz-b-000394464
dc.description.abstract
The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We present four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 3-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, important criteria for the comparability of scientific results across experiments and centers.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Cold Spring Harbor Laboratory
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging
en_US
dc.type
Working Paper
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
bioRxiv
ethz.size
19 p.
en_US
ethz.publication.place
Cold Spring Harbor, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
en_US
ethz.date.deposited
2020-01-27T15:07:18Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-28T11:51:09Z
ethz.rosetta.lastUpdated
2022-03-29T00:51:59Z
ethz.rosetta.versionExported
true
ethz.COinS
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