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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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“A high-resolution MR image was obtained for each subject and co-registered with the corresponding PET images; each matched pair of MR and PET data sets was linearly transformed into stereotaxic space (Collins et al. 1994; Talairach & Tournoux 1988).”
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“All images in a session were realigned to the mean image of a run to correct for head movement, and normalized into the stereotaxic MNI space (Collins et al., 1994).”
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“Each subjectis brain MRI was subsequently registered to an average of 305 brains aligned with Talairach space ( Collins, Neelin, Peters, & Evans, 1994; Talairach & Tournoux, 1988), using Oxford Centre for Functional Magnetic Resonance Imaging of the Brain Software Library tools (FMRIB, Oxford, UK; www.fmrib.ox.ac.uk/f…”
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“Subsequently, it was spatially normalized to the MNI single subject template (Evans et al., 1992; Collins et al., 1994; Holmes et al., 1998) using linear proportions and a nonlinear sampling algorithm (Ashburner and Friston, 2003a,b).”
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“This was carried out by first aligning the functional to the structural data with a six degrees-of-freedom transform using SPM software, and then aligning the structural data to the MNI305 template using a twelve degrees-of-freedom alignment procedure (Collins et al., 1994).”
Selective attention to sound location or pitch studied with fMRI
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“While the Talairach atlas is based on a single brain, the MNI defined a new standard brain by averaging several magnetic resonance (MR) scans of human subjects (Collins et al., 1994; for a review, see Brett et al., 2002).”
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“…scaled in grey-level intensities across subjects to match the mean level of the reference image, registered into a standard target template space (Collins et al., 1994) optimizing global then local alignment between reference and subject via successive affine transformations, the first on the…”
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“Preprocessing of the data used SPM5 realignment, reslicing with sinc interpolation, normalization to the MNI coordinate system (Montreal Neurological Institute) ( Collins, Neelin, Peters, & Evans, 1994 ), and spatial smoothing with a 6-mm full width at half maximum isotropic Gaussian kernel.”
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Multimodality image registration with software: state-of-the-art
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“T1-weighted images of each subject’s brain were transformed into standardized stereotaxic space(24) using automated feature-matching to the Montreal Neurological Institute template.(25) PET frames were smoothed (FWHM 4mm), summed, registered to the corresponding MRI,(26) and transformed into standardized stereotaxic s…”
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“[35].”
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“Briefly, the native MRI scans were registered into standardized stereotaxic space using a linear transformation (21) and corrected for nonuniformity artifacts (22).”
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“The T1w, T2w, PD, and FLAIR images were linearly co-registered using a 6 parameter rigid registration (Collins et al., 1994).”
MethodSelf-citationWhat went where? Impaired object‐location learning in patients with right hippocampal lesions
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“The 75 postoperative, T1-weighted, digitized MRI scans were first transformed into standardized Talairach space (Talairach and Tournoux, 1988) by means of an automatic registration algorithm (Collins et al., 1994) to control for differences in head size and orientation.”
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“The resulting registered MRIs were resampled and averaged [Collins et al., 1994; Kovacevic et al., 2005].”
MethodFunctional connectivity in patients with idiopathic generalized epilepsy
Moeller, F., Maneshi, M., Pittau, F., Gholipour, T., Bellec, P., Dubeau, F., Grova, C., & Gotman, J. (2011) · Epilepsia · doi:10.1111/j.1528-1167.2010.02938.x · Cited by 102
“FMRI response to GSWD intersubject group analysis The EPI images were motion corrected and smoothed (6-mm full width at half maximum, FWHM) using the software package from the Brain Imaging Center (BIC) of the Montreal Neurological Institute (http://www.bic.mni. mcgill.ca/ServicesSoftware/HomePage).”
MethodInfluentialSzulc, K. U., Timmons, B. W., Bouffet, É., Laughlin, S., de Medeiros, C. B., Skocic, J., Lerch, J. P., & Mabbott, D. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.02.021 · Cited by 103
“Here, an average representation of the three scans from each participant was created with a combination of linear and iterative nonlinear registrations, performed using ANIMAL (Collins et al., 1994) and ANTS (Avants and Gee, 2004; Avants et al., 2011), respectively.”
MethodInfluentialRasser, P. E., Johnston, P., Lagopoulos, J., Ward, P. B., Schall, U., Thienel, R., Bender, S., Toga, A. W., & Thompson, P. M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.054 · Cited by 100
“Each subject's structural MR data were aligned with the ICBM53 template (Collins et al., 1994) in order to reduce individual proportional differences across subjects followed by an intensity correction to reduce scanner inhomogeneity due to radio frequency bias (Sled et al., 1998 ).”
MethodVafaee, M. S., Meyer, E., Marrett, S., Paus, T., Evans, A. C., & GJEDDE, A. H. (1999) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-199903000-00005 · Cited by 101
“The magnetic resonance images were transformed into stereotaxic coordinates (Talairach and Tournoux, 1988) using an automatic registration algorithm (Collins et al., 1994).”
MethodSelf-citationDecreased Gray Matter Concentration in the Lateral Geniculate Nuclei in Human Amblyopes
Barnes, G. R., Li, X., Thompson, B. S., et al. (2010) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.09-3931 · Cited by 102
Anatomically Informed Basis Functions
Kiebel, S. J., Goebel, R., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.1999.0542 · Cited by 100
Kim, G. H., Jeon, S., Im, K., Kwon, H., Lee, B. H., Kim, G. Y., Jeong, H., Han, N. E., Seo, S. W., Cho, H., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0123251 · Cited by 101
“Briefly, native MRI images were registered into a standardized stereotaxic space using linear transformation [25].”
MethodThe Faces of Predictive Coding
Brodski, A., Paasch, G.-F., Helbling, S., & Wibral, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1529-14.2015 · Cited by 99
“uk/spm) in MNI space (Collins et al., 1994) obtaining an individual transformation matrix for each subject.”
BackgroundMethod3D comparison of low, intermediate, and advanced hippocampal atrophy in MCI
Apostolova, L. G., Thompson, P. M., Green, A. E., Hwang, K., Zoumalan, C., Jack, C. R., Harvey, D. J., Petersen, R. C., Thal, L. J., Aisen, P. S. S., et al. (2010) · Human Brain Mapping · doi:10.1002/hbm.20905 · Cited by 100
“Baseline and follow-up images were each registered separately to the ICBM53 standardized brain template using a nine-parameter linear transformation [Collins et al., 1994] and subjected to intensity normalization [Shattuck et al., 2001].”
MethodNossin‐Manor, R., Card, D., Morris, D., Noormohamed, S., Shroff, M. M., Whyte, H. E. A., Taylor, M. J., & Sled, J. G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.086 · Cited by 98
“(1) Rigid body registration of T2w volumes was accomplished using a target model, a 30 week neonate for the preterm scans and a 42 week infant for the TEA scans (Collins et al., 1994; Kovacevic et al., 2005).”
MethodInfluentialYoon, U., Lee, J., Im, K., Shin, Y., Cho, B. H., Kim, I. Y., Kwon, J. S., & Kim, S. I. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.021 · Cited by 101
“This was done using automatic registration software that used a 3D cross correlation approach to match the single MRI volume with the intensity average of 305 MRI brain volumes previously aligned in standardized stereotaxic space (Collins et al., 1994).”
MethodAcute vagus nerve stimulation using different pulse widths produces varying brain effects
Mu, Q., Bohning, D. E., Nahas, Z. H., Walker, J., Anderson, B. S., Johnson, K. A., Denslow, S., Lomarev, M. P., Moghadam, P., Chae, J., et al. (2004) · Biological Psychiatry · doi:10.1016/j.biopsych.2003.12.004 · Cited by 100
Localized Cortical Thinning in Patients with Obstructive Sleep Apnea Syndrome
Joo, E. Y., Jeon, S., Kim, S. T., Lee, J., & Hong, S. B. (2013) · SLEEP · doi:10.5665/sleep.2876 · Cited by 106
Towards a method for automated classification of1H MRS spectra from brain tumours
Tate, A. R., Griffiths, J. R., Martínez-Pérez, I., Moreno, À., Barba, I., Cabañas, M. E., Watson, D. M., Alonso, J., Bartumeus, F., Isamat, F., et al. (1998) · NMR in Biomedicine · doi:10.1002/(sici)1099-1492(199806/08)11:4/5 · Cited by 126
Zamora-López, G., Cheng, Y., Deco, G., Kringelbach, M. L., & Zhou, C. (n.d.) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 99
Scale and pattern of atrophy in the chronic stages of moderate-severe TBI
Green, R., Colella, B., Maller, J. J., Bayley, M. T., Glazer, J., & Mikulis, D. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00067 · Cited by 101
“Cell death may occur due to delayed apoptotic mechanisms (Colicos et al., 1996; Bramlett et al., 1997a,b; Dixon et al., 1999; Williams et al., 2001; Coulson et al., 2008) secondary to trans-neuronal degeneration (Gennarelli and Graham, 1998; Tate and Bigler, 2000) arising from disconnections within…”
BackgroundWu, O., Batista, L. M., Lima, F. O., Vangel, M. G., Furie, K. L., & Greer, D. M. (2011) · Stroke · doi:10.1161/strokeaha.110.594879 · Cited by 100
Neural correlates of mystical experience.
Cristofori, I., Bulbulia, J. A., Shaver, J., Wilson, M. S., Krueger, F., & Grafman, J. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.11.021 · Cited by 65
Implementation and application of a brain template for multiple volumes of interest
Hammers, A., Koepp, M. J., Free, S. L., Brett, M., Richardson, M. P., Labbé, C., Cunningham, V. J., Brooks, D. J., & Duncan, J. S. (2002) · Human Brain Mapping · doi:10.1002/hbm.10016 · Cited by 101
“Further refinement of shape matching algorithms such as SPM [Ashburner and Friston, 1999], AIR [Woods et al., 1993] and MNI_AutoReg/ANIMAL [Collins et al., 1994] may therefore not reduce this difference substantially, as it seems due to intersubject differences that are not reducible with these…”
BackgroundMethodResultInfluentialNeural Systems Underlying Decisions about Affective Odors
Rolls, E. T., Fabian Grabenhorst, B., & Parris, B. A. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21231 · Cited by 101
“Preprocessing of the data used SPM5 realignment, reslicing with sinc interpolation, normalization to the MNI coordinate system (Montreal Neurological Institute; Collins, Neelin, Peters, & Evans, 1994 ), and spatial smoothing with a 6-mm FWHM isotropic Gaussian kernel.”
MethodThe left inferior frontal gyrus is crucial for reading the mind in the eyes: Brain lesion evidence
Monte, O. D., Schintu, S., Pardini, M., Berti, A., Wassermann, E. M., Grafman, J. H., & Krüeger, F. (2014) · Cortex · doi:10.1016/j.cortex.2014.05.002 · Cited by 102
Valet, M., Pfab, F., Sprenger, T., Wöller, A. A., Zimmer, C., Behrendt, H., Ring, J., Darsow, U. G., & Tölle, T. R. (2007) · Journal of Investigative Dermatology · doi:10.1038/sj.jid.5701002 · Cited by 100
“Institute) (Collins et al., 1994), and resampled with 3 3 3 mm voxel size.”
MethodNeural correlates of social approach and withdrawal in patients with major depression
Derntl, B., Seidel, E., Eickhoff, S. B., Kellermann, T., Gur, R. C., Schneider, F. W., & Habel, U. (2011) · Social Neuroscience · doi:10.1080/17470919.2011.579800 · Cited by 102
“The mean functional image was subsequently normalized to the Montreal Neurological Institute (MNI) single-subject template (Collins, Neelin, Peters, & Evans, 1994 ; Evans et al, 1992 ), using linear proportions and a nonlinear sampling as derived from a segmentation algorithm (Ashburner & Friston, 2005 ).”
MethodRichards, J. E., & Xie, W. (2015) · Advances in child development and behavior · doi:10.1016/bs.acdb.2014.11.001 · Cited by 100
Lee, M. A., Smith, S. M., Palace, J. A., Narayanan, S., Silver, N., Minicucci, L., Filippi, M., Miller, D. H., Arnold, D. L., & Matthews, P. M. (1999) · Brain · doi:10.1093/brain/122.7.1261 · Cited by 100
“Image segmentation and analysis were performed with software developed at the Montreal Neurological Institute (Collins et al., 1994).”
MethodThe Danish PET/depression project: PET findings in patients with major depression
Videbech, P., Ravnkilde, B., PEDERSEN, A. R., Egander, A., LANDBO, B., Rasmussen, N. A., Andersen, F. L., Stødkilde‐Jørgensen, H., GJEDDE, A. H., & ROSENBERG, R. (2001) · Psychological Medicine · doi:10.1017/s0033291701004469 · Cited by 100
“Afterwards, the PET image of each subject in the resting situation was co-registered to the MR image using the MINC-TRACC algorithm (Collins et al. 1994), yielding a linear 6-parameter regression.”
MethodFonov, V., Troter, A. L., Taso, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.08.057 · Cited by 99
“, the MNI template) (Collins et al., 1994) revolutionized neuroimaging by enabling group analysis and allowing researchers to share, compare and validate their results.”
Self-citationBody expressions of emotion do not trigger fear contagion in autism spectrum disorder
Hadjikhani, N. K., Joseph, R. M., Manoach, D. S., Naik, P., Snyder, J., Dominick, K. C., Hoge, R. D., den Stock, J. V., Tager-Flusberg, H. B., & de Gelder, B. (2009) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsn038 · Cited by 106
“Volumes containing the estimated effect size and associated standard error for the primary contrast (fearful vs neutral) at each voxel were then registered to a standard space based on the Montreal Neurological Institute (MNI) template (Collins et al., 1994).”
MethodIshibashi, R., Pobric, G., Saito, S., & Ralph, M. A. L. (2016) · Cognitive Neuropsychology · doi:10.1080/02643294.2016.1188798 · Cited by 101
Gene therapy reduces Parkinson’s disease symptoms by reorganizing functional brain connectivity
Niethammer, M., Tang, C. C., Vo, A., Nguyen, N., Spetsieris, P. G., DHAWAN, V. M., Ma, Y., SMALL, M. P., Feigin, A., During, M. J., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aau0713 · Cited by 99
“PDRP changes also differed significantly across the three groups [F(2, 44) = 6.”
BackgroundInfluentialNoninvasive Brain Stimulation for Persistent Postconcussion Symptoms in Mild Traumatic Brain Injury
Koski, L. M., Kolivakis, T., Yu, C., Chen, J., Delaney, S., & Ptito, A. (2014) · Journal of Neurotrauma · doi:10.1089/neu.2014.3449 · Cited by 101
“30 Data from each individual run were then normalized to the MNI305 template using an in-house algorithm 31 and combined together using a fixed-effects analysis for the following comparisons: 1) verbal WM minus verbal control condition and 2) nonverbal WM minus nonverbal control condition.”
Comparison of spatial normalization procedures and their impact on functional maps
Crivello, F., Schormann, T., Tzourio‐Mazoyer, N., Roland, P. E., Zilles, K., & Mazoyer, B. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10047 · Cited by 98
“…to a comparison with the simplest linear affine transformation [Andersson and Thurfjell, 1997; Ashburner et al., 1998; Ashburner and Friston, 2000; Collins et al., 1994; Davatzikos, 1996; Kochunov et al., 1999; Lancaster et al., 1999; Meyer et al., 1999; Minoshima et al., 1994; Rizzo et al.,…”
BackgroundMacaluso, E., Eimer, M., Frith, C., & Driver, J. (2003) · Experimental Brain Research · doi:10.1007/s00221-002-1335-y · Cited by 102
“To allow intersubject analysis, images were normalised to the Montreal Neurological Institute (MNI) standard space (Collins et al. 1994), using the mean of the functional images.”
MethodLocalization of hand motor activation in Broca's pli de passage moyen
Boling, W., Olivier, A., Bittar, R. G., et al. (1999) · Journal of neurosurgery · doi:10.3171/jns.1999.91.6.0903 · Cited by 99
Cortical thinning related to periventricular and deep white matter hyperintensities
Seo, S. W., Lee, J.-M., Im, K., Park, J.-S., Kim, S.-H., Kim, S. T., Ahn, H.-J., Chin, J., Cheong, H.-K., Weiner, M. W., et al. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.12.003 · Cited by 95
“The native MRI images were registered into a standardized stereotaxic space, using a linear transformation (Collins et al., 1994).”
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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