Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Author:

Taha Alaa,Gilmore Greydon,Abbass Mohamad,Kai Jason,Kuehn Tristan,Demarco John,Gupta Geetika,Zajner Chris,Cao DanielORCID,Chevalier Ryan,Ahmed Abrar,Hadi Ali,Karat Bradley G.,Stanley Olivia W.,Park Patrick J.,Ferko Kayla M.,Hemachandra Dimuthu,Vassallo Reid,Jach Magdalena,Thurairajah Arun,Wong Sandy,Tenorio Mauricio C.,Ogunsanya Feyi,Khan Ali R.ORCID,Lau Jonathan C.ORCID

Abstract

AbstractTools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 – 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines.

Funder

Canada Foundation for Innovation

Canada First Research Excellence Fund

Start-up funding through the Department of Clinical Neurological Sciences at University of Western Ontario

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

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