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Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space
Abstract
OBJECTIVE: In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. MATERIALS AND METHODS: The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. RESULTS: With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. CONCLUSION: We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
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“It has been shown that the automatic stereotaxic transformation is as accurate as the manual procedure but shows higher stability (Collins et al., 1994).”
From Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood (2005) · cites this paper as Collins et al., 1994
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“The ®rst PET image was coregistered with an average MRI volume of 305 normal subjects (Montreal MNI dataset; Collins et al., 1994) and mapped into standardized stereotaxic space (Talairach and Tournoux, 1988) using a nineparameter af®ne transformation. t-Statistical maps were calculated after a…”
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MethodSelf-citationJobst, B., Hindriks, R., Laufs, H., Tagliazucchi, E., Hahn, G., Ponce‐Alvarez, A., Stevner, A. B. A., Kringelbach, M. L., & Deco, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-04522-x · Cited by 113
Neurobehavioral correlates of obesity are largely heritable
Vainik, U., Baker, T. E., Dadar, M., Zeighami, Y., Michaud, A., Zhang, Y., Alanis, J. C. G., Mišić, B., Collins, D. L., & Dagher, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718206115 · Cited by 118
Self-citationBrain Volumes and Surface Morphology in Monozygotic Twins
White, T. J. H. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.5.486 · Cited by 117
“The use of stereotactic techniques have been shown to be accurate for measures of cerebral and lobar volumes (Collins et al., 1994; Andreasen et al., 1996).”
Chou, Y.-Y., Leporé, N., Avedissian, C., Madsen, S. K., Parikshak, N., Hua, X., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., Toga, A. W., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.015 · Cited by 117
“To adjust for global differences in brain positioning and scale, we spatially normalized all images to the ICBM-53 average brain template with a 9-parameter linear transformation using the Minctracc algorithm (Collins et al., 1994).”
Teipel, S. J., Pruessner, J. C., Faltraco, F., Born, C., Rocha-Unold, M., Evans, A. C., Möller, H. ‐., & Hampel, H. J. (2006) · Journal of Neurology · doi:10.1007/s00415-006-0120-4 · Cited by 118
“Prior to volumetric measurements, all MRI volumes were corrected for image intensity non-uniformities [30], mapped by affine stereotaxtic transformation [31] into coordinates based on the Talairach atlas [32], and resampled onto a 1 mm voxel grid.”
MethodSelf-citationDissociable brain regions process object meaning and object structure during picture naming
Whatmough, C. E., Chertkow, H. M., Murtha, S. J. E., et al. (2002) · Neuropsychologia · doi:10.1016/s0028-3932(01)00083-5 · Cited by 117
Cortical deafness to dissonance
Peretz, I. (2001) · Brain · doi:10.1093/brain/124.5.928 · Cited by 119
“2 CT scan (1.5 mm slices) of I.R.'s brain, transformed into the standardized stereotaxic space of Talairach and Tournoux (Talairach and Tournoux, 1988; following the procedure of Collins et al., 1994).”
InfluentialMullen, K. T., Dumoulin, S. O., McMahon, K. L., de Zubicaray, G. I., & Hess, R. F. (2007) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2007.05302.x · Cited by 117
“Thus VO was defined as a colour-sensitive region that Peak t and corresponding P values are listed, corrected for multiple comparisons (Worsley et al., 1996; Worsley et al., 2002), with their corresponding x, y and z stereotaxic coordinates (Talairach & Tournoux, 1988; Collins et al., 1994).”
InfluentialSimple and Associative Recognition Memory in the Hippocampal RegionPreprint
Stark, C. E. L., & Squire, L. R. (2001) · Learning & Memory · doi:10.1101/lm.40701 · Cited by 115
“A nine-parameter transformation matrix was calculated to transform (Collins et al. 1994) the structural MRI from each participant’s first scanning session to conform to the atlas of Talairach and Tournoux (1988).”
MethodCorticomuscular coherence in acute and chronic stroke
von Carlowitz-Ghori, K., Bayraktaroğlu, Z., Hohlefeld, F. U., Losch, F., Curio, G., & Nikulin, V. V. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.11.006 · Cited by 122
Human amygdala reactivity is diminished by the β-noradrenergic antagonist propranolol
Hurlemann, R., Walter, H., Rehme, A. K., Kukolja, J., Santoro, S. C., Schmidt, C., Schnell, K., Mußhoff, F., Keysers, C., Maier, W., et al. (2010) · Psychological Medicine · doi:10.1017/s0033291709992376 · Cited by 119
Müller, H.-P., Unrath, A., Ludolph, A. C., & Kassubek, J. (2007) · Physics in Medicine and Biology · doi:10.1088/0031-9155/52/6/n01 · Cited by 119
“An alternative approach was to use automated brain registration algorithms (Collins et al 1994, Friston et al 1995).”
MethodBrain morphology in antipsychotic-naïve schizophrenia: A study of multiple brain structures
Cahn, W., Pol, H. E. H., Bongers, M. L., Schnack, H. G., Mandl, R. C. W., van Haren, N. E. M., Durston, S., Koning, H., van der Linden, J. A., & Kahn, R. S. (2002) · The British Journal of Psychiatry · doi:10.1192/bjp.181.43.s66 · Cited by 115
van Hartevelt, T. J., Cabral, J., Deco, G., Møller, A., Green, A. L., Aziz, T. Z., & Kringelbach, M. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086496 · Cited by 114
“The transformed T1-weighted image was then mapped to the T1 template of ICBM152 in MNI space [31].”
MethodDecision-Making, Errors, and Confidence in the Brain
Rolls, E. T., Fabian Grabenhorst, B., & Deco, G. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00571.2010 · Cited by 123
van der Schot, A. C., Vonk, R., Brans, R. G. H., van Haren, N. E. M., Koolschijn, P. C. M. P., Nuboer, V., Schnack, H. G., van Baal, G. C. M., Boomsma, D. I., Nolen, W. A., et al. (2009) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2008.541 · Cited by 114
“4 Lobar white matteri 297 (52) 310 (55) 306 (39) −2.”
Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems
Wu, J., Ngo, G. H., Greve, D. N., Li, J., He, T., Fischl, B. R., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Human Brain Mapping · doi:10.1002/hbm.24213 · Cited by 113
Effects of prosodic emotional intensity on activation of associative auditory cortex
Ethofer, T., Anders, S., Wiethoff, S., Erb, M., Herbert, C., Saur, R., Grodd, W., & Wildgruber, D. (2006) · Neuroreport · doi:10.1097/01.wnr.0000199466.32036.5d · Cited by 114
“Beta estimates (mean7standard error) corresponding to distinct intensity steps of emotional intonations have been plotted for the most signi¢cant voxel of the cluster in the right (b) [Montreal Neurological Institute (MNI) coordinates: x¼60, y¼ 21, z¼3] and left (c) (MNI coordinates: x¼ 54, y¼ 12, z¼0) cortex in mid-s…”
MethodStimulus-Dependent BOLD and Perfusion Dynamics in Human V1
Hoge, R. D., Atkinson, J., Gill, B., Crelier, G. R., Marrett, S., & Pike, G. B. (1999) · NeuroImage · doi:10.1006/nimg.1999.0443 · Cited by 114
“The V1 visual field maps were resampled onto the voxel grids used in subsequent experiments according to the computed transformation required to align high-resolution anatomic images acquired at the beginning of each scanning session (Collins et al., 1994).”
MethodHemodynamic and metabolic responses to activation, deactivation and epileptic discharges
Stefanovic, B., Warnking, J. M., Kobayashi, E., Bagshaw, A. P., Hawco, C. S., Dubeau, F., Gotman, J., & Pike, G. B. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.05.038 · Cited by 115
“1 shows a slice of BOLD and CBF ROIs, summed over all subjects after registration (Collins et al., 1994) with the Montreal Neurological Institute template brain (Evans et al.”
Method4D deformation modeling of cortical disease progression in Alzheimer's dementia
Janke, A. L., de Zubicaray, G. I., Rose, S., Griffin, M., Chalk, J. B., & Galloway, G. J. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1243 · Cited by 116
“ca/software/mni_autoreg/)) with a 3D step size of 2 mm (16).”
MethodDuncan, G. H., Kupers, R. C., Marchand, S., Villemure, J., Gybels, J., & Bushnell, M. C. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.6.3326 · Cited by 118
“…of 3 of 5Each subject’s PET volumes were transformed into the Talairach patients questioned; of the remaining 2, 1 died and 1 couldcoordinate system (Talairach and Tournoux 1988) and coregistered not be reached; see Table 2).with each other by using automated methods of Collins et al. (1994).”
MethodEtchamendy, N., Konishi, K., Pike, G. B., Marighetto, A., & Bohbot, V. D. (2011) · Hippocampus · doi:10.1002/hipo.20948 · Cited by 137
“BOLD signal images were spatially smoothed (6 mm Gaussian Kernel), corrected for motion, and linearly transformed into standard stereotaxic space (Talairach and Tournoux, 1988) using in-house software (Collins et al., 1994).”
Carbonell, F., Bellec, P., & Shmuel, A. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0065 · Cited by 112
“For each subject, the mean motion-corrected volume of all runs was co-registered with an individual T1 scan using Minctracc (Collins et al., 1994), which was itself nonli-nearly transformed to the Montreal Neurological Institute (MNI) nonlinear template using the CIVET pipeline (Zijden-bos et al., 2002).”
InfluentialOkazawa, H., Yamauchi, H., Sugimoto, K., et al. (2001) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-200112000-00012 · Cited by 114
“The MRI image of each subject was transformed into Talairach space automatically (Collins et al., 1994; Talairach and Tournoux, 1988).”
MethodPlaque and tangle imaging and cognition in normal aging and Alzheimer's disease
Braskie, M. N., Klunder, A. D., Hayashi, K. M., Protas, H. D., Kepe, V., Miller, K. J., Huang, S.-C., Barrio, J. R., Ercoli, L. M., Siddarth, P., et al. (2008) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2008.09.012 · Cited by 120
The Cortical Neuroanatomy Related to Specific Neuropsychological Deficits in Alzheimer's Continuum
Kang, S. H., Park, Y. H., Lee, D., Kim, J. P., Chin, J., Ahn, Y., Park, S. B., Kim, H. J., Jang, H., Jung, Y. H., et al. (2019) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2019.18.3.77 · Cited by 114
Carbon, M., Ma, Y., Barnes, A., DHAWAN, V. M., Chaly, T., Ghilardi, M. F., & Eidelberg, D. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.014 · Cited by 111
Discovery and replication of gene influences on brain structure using LASSO regression
Omid Kohannim, A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00115 · Cited by 114
“, 2001) using a 9-parameter transformation (three translations, three rotations, and three scales; Collins et al., 1994).”
MethodInfluentialAutomatic quantification of multiple sclerosis lesion volume using stereotaxic space
Zijdenbos, A. P., Evans, A. C., Riahi, F., Sled, J. G., Chui, J., & Kollokian, V. (1996) · Lecture notes in computer science · doi:10.1007/bfb0046984 · Cited by 113
“Using an automated 3D image registration technique [3], each new source volume is registered with a target volume.”
MethodSelf-citationStrafella, A. P., & Paus, T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.6.2624 · Cited by 113
“…with a 14-mm Hanning filter, normalized for differences in global CBF, co-registered with the individual MRIs (Woods et al. 1993) and transformed into standardized stereotaxic space (Talairach and Tournoux 1988) by means of an automated feature-matching algorithm (Collins et al. 1994).”
MethodCerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template
Manera, A. L., Dadar, M., Fonov, V., & Collins, D. L. (2020) · Scientific Data · doi:10.1038/s41597-020-0557-9 · Cited by 114
Self-citationFahim, C., Yoon, U., Das, S., Lyttelton, O., Chen, J., Arnaoutelis, R., Rouleau, G., Sandor, P., Frey, K., Brandner, C., et al. (2010) · Cortex · doi:10.1016/j.cortex.2009.06.008 · Cited by 107
Self-citationInvestigating audiovisual integration of emotional signals in the human brain
Ethofer, T., Pourtois, G., & Wildgruber, D. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)56019-4 · Cited by 120
“Coordinates of activation clusters are given in MNI space (Montreal Neurological Institute; Collins et al., 1994).”
MethodMultichannel matching pursuit and EEG inverse solutions
Durka, P. J., Matysiak, A., Montes, E. M., Valdes-Sosa, P. A., & Blinowska, K. J. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.04.001 · Cited by 108
Metadata
- Authors
- Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C.
- Affiliations
- Montreal Neurological Institute and Hospital (CA)
- Venue
- Journal of Computer Assisted Tomography
- Published
- 1994-03
- Volume
- 18
- Issue
- 2
- Pages
- 192-205
- ISSN
- 0363-8715 (ISSN-L)
- OpenAlex
- W2141796362
- Cited by
- 3,431
- References
- 0
- Citations by year
- 2026: 57 · 2025: 76 · 2024: 100 · 2023: 76 · 2022: 85 · 2021: 134 · 2020: 103 · 2019: 110 · 2018: 108 · 2017: 102 · 2016: 114 · 2015: 154 · 2014: 157 · 2013: 160 · 2012: 160
- FWCI
- 37.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Tomography (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Keywords
- 3D registration, automatic image registration, interobserver variability
- MeSH terms
- Algorithms, Basal Ganglia, Brain, Brain Mapping, Cerebellum, Cerebral Cortex, Cerebral Ventricles, Corpus Callosum, Humans, Image Enhancement, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Structural, Stereotaxic Techniques
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