Open AccessA framework for evaluating correspondence between brain images using anatomical fiducials
Author(s) -
Lau Jonathan C.,
Parrent Andrew G.,
Demarco John,
Gupta Geetika,
Kai Jason,
Stanley Olivia W.,
Kuehn Tristan,
Park Patrick J.,
Ferko Kayla,
Khan Ali R.,
Peters Terry M.
Publication year - 2019
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24693
Subject(s) - fiducial marker , computer science , voxel , artificial intelligence , protocol (science) , spatial normalization , neuroimaging , set (abstract data type) , template , neuroanatomy , computer vision , pattern recognition (psychology) , neuroscience , medicine , psychology , alternative medicine , pathology , programming language
Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject‐to‐template and template‐to‐template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point‐based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.