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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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“Each fMRI dataset was corrected for slice timing; a rigid-body motion was then estimated for each time frame, both within and between runs, as well as between one fMRI run and the T1 scan for each subject (Collins et al., 1994).”
Dance and music share gray matter structural correlates
Karpati, F. J., Giacosa, C., Foster, N. E. V., Penhune, V. B., & Hyde, K. L. (2016) · Brain Research · doi:10.1016/j.brainres.2016.11.029 · Cited by 88
“They were registered to the ICBM152 nonlinear model (Collins et al., 1994; Grabner et al., 2006) with 12 degrees of freedom for registration, and corrected for signal intensity nonuniformity (Sled et al., 1998).”
Human V5/MT+: comparison of functional and cytoarchitectonic data
Wilms, M., Eickhoff, S. B., Specht, K., Amunts, K., Shah, N. J., Maliković, A., & Fink, G. R. (2005) · Anatomy and Embryology · doi:10.1007/s00429-005-0064-y · Cited by 90
“The 3D-anatomical data set was coregis- tered to the mean EPI image and then spatially normalised to the stereotactic space defined by the singlesubject template provided by the Montreal Neurological Institute (MNI) (Evans et al. 1992; Collins et al. 1994; Holmes et al. 1998).”
MethodSexual Dimorphism in the Corpus Callosum: Methodological Considerations in MRI Morphometry
Bermudez, P., & Zatorre, R. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0772 · Cited by 90
“Images were registered to each other and to a Talairach-like stereotaxic space during an algorithmic, 9-parameter registration process (Collins et al., 1994), resampled to 181 ϫ 217 ϫ 181 slices at 1 ϫ 1 ϫ 1 mm resolution and nonuniformity corrected (Sled et al., 1998).”
MethodGender consistency and difference in healthy adults revealed by cortical thickness
Lv, B., Li, J., He, H., Li, M., Zhao, M., Ai, L., Yan, F., Xian, J., & Wang, Z. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.020 · Cited by 91
“, 1998) and automatically registered into the standardized stereotaxic space using a linear transform (Collins et al., 1994).”
MethodPatterns of cortical thinning in idiopathic rapid eye movement sleep behavior disorder
Rahayel, S., Montplaisir, J. Y., Monchi, O., Bedetti, C., Postuma, R. B., Brambati, S. M., Carrier, J., Joubert, S., Latreille, V., Jubault, T., et al. (2014) · Movement Disorders · doi:10.1002/mds.25820 · Cited by 89
Neural Correlates of Artificial Grammar Learning
Skosnik, P. D., Mirza, F., Gitelman, D. R., Parrish, T. B., Mesulam, M. M., & Reber, P. J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1291 · Cited by 87
“Finally, all of the runs were transformed (Collins et al., 1994) to conform to the atlas of Talairach and Tournoux (1988) with a final voxel size of 2.”
InfluentialEffect of mild cognitive impairment on the patterns of neural activity in early Parkinson's disease
Nagano‐Saito, A., Habak, C., Mejia‐Constain, B., Degroot, C., Monetta, L., Jubault, T., Bedetti, C., Lafontaine, A., Chouinard, S., Soland, V., et al. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.06.025 · Cited by 89
“The resulting effects and SD images were then nonlinearly transformed into standard proportional stereotaxic space (ICBM152 template) using anatomic MRI to template transformation parameters, using a feature-matching algorithm (Collins et al., 1994; Zijdenbos et al., 2002).”
MethodThree‐dimensional surface maps link local atrophy and fast ripples in human epileptic hippocampus
Ogren, J. A., Wilson, C. L., Bragin, A., et al. (2009) · Annals of Neurology · doi:10.1002/ana.21703 · Cited by 89
Age-related deficits in the ability to encode contextual change: A place cell analysis
Oler, J. A., & Markus, E. J. (2000) · Hippocampus · doi:10.1002/1098-1063(2000)10:3 · Cited by 58
Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features
Toews, M., & Wells, W. (2012) · Medical Image Anal. · doi:10.1016/j.media.2012.11.002 · Cited by 98
“Image registration can be used as a general alignment solution, where the model is represented as an average template [20, 6] or template collection [11].”
BackgroundMethodInfluentialCopping, N. A., Christian, S., Ritter, D., Islam, M. S., Buscher, N., Żółkowska, D., Pride, M. C., Berg, E. L., LaSalle, J. M., Ellegood, J., et al. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx289 · Cited by 86
Word and nonword repetition in bilingual subjects: A PET study
Klein, D., Watkins, K. E., Zatorre, R. J., & Milner, B. A. (2005) · Human Brain Mapping · doi:10.1002/hbm.20174 · Cited by 89
“Each MRI/PET dataset was then linearly resampled into the standardized stereotaxic coordinate system of Talairach and Tournoux [1988] via an automated feature-matching algorithm [Collins et al., 1994].”
MethodThe Representation of Information About Taste and Odor in the Orbitofrontal Cortex
Rolls, E. T., Critchley, H., Verhagen, J. V., & Kadohisa, M. (2009) · Chemosensory Perception · doi:10.1007/s12078-009-9054-4 · Cited by 87
“2008), and this region is at the anterior end of the human insula (with Y coordinates; Collins et al. 1994) in the approximate range of 14 to 3 mm (de Araujo et al.”
BackgroundMoon, S. Y., Barton, J. J. S., Mikulski, S., Polli, F. E., Cain, M. S., Vangel, M. G., Hämäläinen, M., & Manoach, D. S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.040 · Cited by 89
Tremblay, P., & Gracco, V. L. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.07.041 · Cited by 87
“In order to compute group data, subject data were transformed into stereotaxic space (Collins et al., 1994), and combined using a mixed effects linear model for the effects (as data) with fixed effects standard deviations taken from the previous analysis.”
MethodHorder, J., Harmer, C. J., Cowen, P. J., & McCabe, C. (2010) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145710000453 · Cited by 89
“Pre-processing of the data used SPM5 realignment, reslicing with sinc interpolation, normalization to the Montreal Neurological Institute (MNI) coordinate system and spatial smoothing with a 6-mm full-width at half maximum isotropic Gaussian kernel and global scaling (Collins et al. 1994).”
Kupers, R. C., Svensson, P. J., & Jensen, T. S. (2004) · Pain · doi:10.1016/j.pain.2003.12.029 · Cited by 86
“For anatomical localization of activation sites, PET images were coregistered to each individual’s MRI (Collins et al., 1994).”
MethodGlobal spatial normalization of human brain using convex hulls.
Lancaster, J. L., Fox, P. T., Downs, H., Nickerson, D., Hander, T. A., Mallah, M. E., Kochunov, P. V., & Zamarripa, F. (1999) · PubMed · Cited by 85
Wu, M., Lu, L. H., Lowes, A., Yang, S., Passarotti, A. M., Zhou, X. J., & Pavuluri, M. N. (2013) · Human Brain Mapping · doi:10.1002/hbm.22368 · Cited by 86
“Individual T1w image was first linearly registered to the Talairach space [Collins et al., 1994; Talairach r 2808 r and Tournoux, 1988].”
BackgroundMethodInfluentialYao, Z., Fu, Y., Wu, J., Zhang, W., Yu, Y., Zhang, Z., Wu, X., Wang, Y., & Hu, B. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-0003-1 · Cited by 89
“These segmented images were spatially normalized to the MNI template space with a 9-parameter (3 translations, 3 rotations and 3 scales) linear transformation using Minctracc algorithm (Collins et al. 1994) for the correction of head tilt and alignment.”
Brain endothelial TAK1 and NEMO safeguard the neurovascular unit
Ridder, D. A., Wenzel, J., Müller, K., Töllner, K., Tong, X., Assmann, J. C., Stroobants, S., Weber, T., Niturad, C. E., Fischer, L., et al. (2015) · The Journal of Experimental Medicine · doi:10.1084/jem.20150165 · Cited by 82
Gruber, T., Trujillo‐Barreto, N. J., Giabbiconi, C., et al. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.004 · Cited by 87
Garver, D. L., Holcomb, J. A., & Christensen, J. D. (2007) · The International Journal of Neuropsychopharmacology · doi:10.1017/s1461145707007730 · Cited by 87
“b Brain region labels and approximate Brodmann areas were obtained using the Talairach Daemon (Collins et al., 1994).”
BackgroundMethodInfluentialNo-Reference Image Quality Metrics for Structural MRI
Woodard, J. P., & Carley-Spencer, M. P. (2006) · Neuroinformatics · doi:10.1385/ni:4:3:243 · Cited by 89
“These images were registered by MNI to their MNI-305 template image (Collins et al., 1994).”
MethodSanford, R., Cruz, A. L. F., Scott, S. C., Mayo, N. E., Fellows, L. K., Ances, B. M., & Collins, D. L. (2017) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000001294 · Cited by 86
Self-citationBrain morphometry using 3D moment invariants
Mangin, J., Poupon, F., Duchesnay, É., Rivière, D., Cachia, A., Collins, D. L., EVANS, A., & Régis, J. M. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.06.016 · Cited by 90
“The 142 T1-weighted brain volumes were stereotaxically transformed using nine parameters [8] to match the Montreal Neurological Institute 305 average template.”
MethodSelf-citationClinical effect of white matter network disruption related to amyloid and small vessel disease
Kim, H. J., Im, K., Kwon, H., Lee, J.-M., Kim, C., Kim, Y. J., Jung, N.-Y., Cho, H., Ye, B. S., Noh, Y., et al. (2015) · Neurology · doi:10.1212/wnl.0000000000001705 · Cited by 85
It is not always tickling: Distinct cerebral responses during perception of different laughter types
Szameitat, D. P., Kreifelts, B., Alter, K., Szameitat, A. J., Sterr, A., Grodd, W., & Wildgruber, D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.028 · Cited by 86
“The transformationmatrix for normalization to theMontrealNeurological Institute (MNI) space (Collins et al., 1994) was calculated on the basis of the structural T1-weighted 3-D data set of each subject and subsequently applied to the functional images.”
Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery
Rivaz, H. J., Chen, S. J.-S., & Collins, D. L. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2354352 · Cited by 83
“They then registered the pseudoUS to the real US using the ANIMAL registration technique [19].”
MethodSelf-citationGray and white matter correlates of the Big Five personality traits
Privado, J., Román, F. J., Saénz-Urturi, C., Burgaleta, M., & Colom, R. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.02.039 · Cited by 86
CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation
Wilke, M., Altaye, M., & Holland, S. K. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00005 · Cited by 83
“…brains were traditionally generated using a linear, affine transformation only, which will scale the image globally to match the template, but will not perform any non-linear feature matching in order to preserve the individual characteristics (e.g., Collins et al., 1994; Wilke et al., 2002, 2008).”
MethodSevy, S., Smith, G. S., Ma, Y., Dhawan, V., Chaly, T., Kingsley, P. B., Kumra, S., Abdelmessih, S., & Eidelberg, D. (2008) · Psychopharmacology · doi:10.1007/s00213-008-1075-1 · Cited by 83
“0001 Montreal Neurological Institute standard space (Collins et al. 1994) Psychopharmacology (2008) 197:549–556 553”
BackgroundAn acute psychosocial stress enhances the neural response to smoking cues
Dagher, A., Tannenbaum, B., Hayashi, T., Pruessner, J. C., & McBride, D. (2009) · Brain Research · doi:10.1016/j.brainres.2009.07.048 · Cited by 80
“Anatomical data were transformed into Montreal Neurological Institute (MNI) space (Collins et al., 1994) using the ICBM152 template.”
Method
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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