AFIDs

An open framework for evaluating correspondence in brain images and teaching neuroanatomy using anatomical fiducial placement

Documentation: https://afids.github.io/afids-protocol/

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Introduction

• The AFIDs protocol is an anatomical fiducial placement protocol that has been validated and used for teaching at a number of local and global BrainHack related events.
  • Human AFIDs protocol
  • Macaca AFIDs protocol
• The protocol and study framework leverage open resources/tools and has been developed with full transparency in mind so that others may freely use, adopt, and modify.
• AFID placement is reproducible, not overtly manually intensive (20-40 minutes once trained), and more sensitive to local registration errors than standard voxel overlap measures.
• 60+ raters trained to date.

References and Resources

Peer-Reviewed Manuscript

• Lau JC, Parrent AG, Demarco J, Gupta G, Park PJ, Ferko K, Khan AR, Peters TM. A framework for evaluating correspondence between brain images using anatomical fiducials. Human Brain Mapping. 2019. [ref1]

Associated Preprint

• Lau JC, Parrent AG, Demarco J, Gupta G, Park PJ, Ferko K, Khan AR, Peters TM. A framework for evaluating correspondence between brain images using anatomical fiducials. bioRxiv. 2018. [ref1]

Conference Abstracts

• Lau JC, Parrent AG, Demarco J, Gupta G, Park PJ, Ferko K, Khan AR, Peters TM. AFIDs: an open framework for evaluating correspondence between magnetic resonance images of the human brain using fiducial placement. F1000 Research. Demo presented at INCF NeuroInformatics in Montreal, QC, Canada. 2018. [ref2]

Open Datasets

• Agile12v2016: Lau JC, MacDougall KW, Arango MF, Peters TM, Parrent AG, Khan AR: Ultra-High Field Template-Assisted Target Selection for Deep Brain Stimulation Surgery. World Neurosurg 103:531–537, 2017. [rdownload1][ref3]

• Colin27: Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC: Enhancement of MR Images Using Registration for Signal Averaging. J Comput Assist Tomogr 22:324–333, 1998. [download2][ref4]

• MNI152NLin2009bAsym: Fonov V, Evans AC, Botteron K, Almli RR, McKinstry RC, Collins LL, et al: Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54:313–327, 2011. [download3][ref5]

• OASIS1: Marcus DS, Fotenos AF, Csernansky JG, Morris JC, Buckner RL: Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults. J Cogn Neurosci 22:2677–2684, 2010. [download4][ref6]

Software

• 3D Slicer: Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al: 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341, 2012. [download5][ref7]

Historical References

• Talairach1957: Talairach J, David M, Tournoux P, Corredor H, Kvasina T: Atlas d’anatomie Stéréotaxique. Repérage Radiologique Indirect Des Noyaux Gris Centraux Des Régions Mésencephalosousoptique et Hypothalamique de l’homme. Paris, France: Masson & Cie, 1957

• Talairach1988: Talairach J, Tournoux P: Co-Planar Stereotaxic Atlas of the Human Brain: 3-D Proportional System: An Approach to Cerebral Imaging. ed 1, Thieme, 1988.

Other Resources

• BrainHack Global 2018:

  • Training Workshop details
  • Tutorial and associated [video]

• 3D Slicer (use version 4.10.2)

• AFIDs-validator; started at BHG18-Western; also see the associated OHBM2020 poster.

• Scicrunch (RRID:SCR_016623)