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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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Stefanovic, B., Warnking, J. M., & Pike, G. B. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.01.036 · Cited by 306
“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.”
Boileau, I., Dagher, A., Leyton, M., Gunn, R. N., Baker, G. B., Diksic, M., & Benkelfat, C. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.12.1386 · Cited by 305
“The MRI volumes were corrected for image intensity nonuniformity(39) and were linearly and nonlinearly transformed into standardized stereotaxic space(40) using automated feature matching to the Montreal Neurological Institute template.(41) Automated MRI tissue type classification and segmentation(42) were applied to…”
MethodA framework to study the cortical folding patterns
Mangin, J., Rivière, D., Cachia, A., Duchesnay, É., Cointepas, Y., Orfanos, D. P., Scifo, P., Ochiai, T., Brunelle, F., & Régis, J. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.019 · Cited by 305
“The 142 T1-weighted brain volumes were stereotaxically transformed using nine parameters (Collins et al., 1994) to match the Montreal Neurological Institute 305 average template.”
MethodAudiovisual integration of emotional signals in voice and face: An event-related fMRI study
Kreifelts, B., Ethofer, T., Grodd, W., Erb, M., & Wildgruber, D. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.06.020 · Cited by 322
“The transformation matrix for normalization to Montreal Neurological Institute (MNI) space (Collins et al., 1994) was calculated based on the structural T1-weighted 3-”
MethodNopoulos, P., Flaum, M., O’Leary, D., & Andreasen, N. C. (2000) · Psychiatry Research: Neuroimaging · doi:10.1016/s0925-4927(99)00044-x · Cited by 303
Structural MR image processing using the brains2 toolbox
Magnotta, V. A., Harris, G., Andreasen, N. C., OʼLeary, D. S., Yuh, W. T. C., & Heckel, D. (2002) · Computerized Medical Imaging and Graphics · doi:10.1016/s0895-6111(02)00011-3 · Cited by 301
Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning
Lerch, J. P., Yiu, A. P., Martinez-Canabal, A., Pekar, T., Bohbot, V. D., Frankland, P. W., Henkelman, R. M., Josselyn, S. A., & Sled, J. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.086 · Cited by 300
“We used an automated intensity-based group-wise registration approach (Collins et al., 1994, 1995; Kovacević et al., 2005) to align all brains in the study into a common coordinate system, yielding an average image of all brains and deformations that relate individual images to this average.”
MethodCheyne, D. O., Bakhtazad, L., & Gaetz, W. C. (2005) · Human Brain Mapping · doi:10.1002/hbm.20178 · Cited by 299
“Group images were superimposed on the MNI (CH) template brain [Collins et al., 1994] using the mri3dX program (http:// www.aston.ac.uk/lhs/research/nri/groups/nrg/mri3dx/ index.jsp) to view 2D or 3D (rendered) images of the ERSAM functional images in template space.”
MethodComparison of functional activation foci in children and adults using a common stereotactic space
Kang, H. C., Burgund, E. D., Lugar, H. M., Petersen, S. E., & Schlaggar, B. L. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00038-7 · Cited by 299
“Two commonly used templates for adult neuroimaging studies are the Talairach atlas, based on an elderly woman (Talairach and Tournoux, 1988), and the Montreal Neurologic Institute atlas, based on a group of young adults (Collins et al., 1994).”
MethodRegional Gray Matter Density Changes in Brains of Patients With Irritable Bowel Syndrome
Seminowicz, D. A., Labus, J. S., Bueller, J. A., Tillisch, K., Naliboff, B. D., Bushnell, M. C., & Mayer, E. A. (2010) · Gastroenterology · doi:10.1053/j.gastro.2010.03.049 · Cited by 303
Feature-selective attention enhances color signals in early visual areas of the human brain
Müller, M. M., Andersen, . K., Trujillo‐Barreto, N. J., Valdes-Sosa, P. A., Malinowski, P., & Hillyard, S. A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606668103 · Cited by 295
Pruessner, J. C. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.12.1342 · Cited by 297
“This included correction for magnetic field non-uniformities (Sled et al., 1998), linear stereotaxic transformation (Collins et al., 1994) into coordinates based on the Talairach atlas (Talairach and Tournoux, 1988) and resampling onto a 1 mm voxel grid before image segmentation using a linear…”
BackgroundMethodThe creation of a brain atlas for image guided neurosurgery using serial histological data
Chakravarty, M. M., Bertrand, G., Hodge, C. P., Sadikot, A. F., & Collins, D. L. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.041 · Cited by 298
Self-citationThe Golden Beauty: Brain Response to Classical and Renaissance Sculptures
Dio, C. D., Macaluso, E., & Rizzolatti, G. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001201 · Cited by 293
“To allow intersubject analysis, images were normalised to the Montreal Neurological Institute (MNI) standard space [18], using the mean of the 162 functional images.”
MethodAn Image-Processing System for Qualitative and Quantitative Volumetric Analysis of Brain Images
Goldszal, A. F., Davatzikos, C. A., Pham, D. L., Yan, M., Bryan, R. N., & Resnick, S. M. (1998) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199809000-00030 · Cited by 295
Gender difference analysis of cortical thickness in healthy young adults with surface-based methods
Im, K., Lee, M.-J., Lee, J., Shin, Y.-W., Kim, I. Y., Kwon, J. M., & Kim, S. I. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.11.042 · Cited by 294
“Spatial normalization to a stereotaxic space was performed using a nine-parameter linear registration (Collins et al., 1994).”
MethodDynamic coupling of whole-brain neuronal and neurotransmitter systems
Kringelbach, M. L., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K., & Deco, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921475117 · Cited by 284
Age and gender predict volume decline in the anterior and posterior hippocampus in early adulthood
Pruessner, J. C., Collins, D. L., Pruessner, M., & Evans, A. C. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-01-00194.2001 · Cited by 292
“, 1998), linear stereotaxic transformation (Collins et al., 1994) into coordinates based on the Talairach atlas (Talairach and Tournoux, 1988), and resampling onto a 1 mm voxel grid before image segmentation using a linear interpolation kernel.”
MethodSelf-citationHyde, K. L., Samson, F., Evans, A. C., & Mottron, L. (2009) · Human Brain Mapping · doi:10.1002/hbm.20887 · Cited by 304
Self-citationEvent-Related fMRI of the Auditory Cortex
Belin, P., Zatorre, R. J., Hoge, R. D., Evans, A. C., & Pike, G. B. (1999) · NeuroImage · doi:10.1006/nimg.1999.0480 · Cited by 296
Self-citationBasten, U., Hilger, K., & Fiebach, C. J. (2015) · Intelligence · doi:10.1016/j.intell.2015.04.009 · Cited by 324
Lafleur, M. F., Jackson, P. L., Malouin, F., Richards, C. L., Evans, A. C., & Doyon, J. (2002) · NeuroImage · doi:10.1006/nimg.2001.1048 · Cited by 292
Self-citationShaw, P., Lerch, J. P., Pruessner, J. C., Taylor, K. N., Rose, A. B., Greenstein, D., Clasen, L., Evans, A., Rapoport, J. L., & Giedd, J. N. (2007) · The Lancet Neurology · doi:10.1016/s1474-4422(07)70106-0 · Cited by 257
Self-citationOn the accuracy of T1 mapping: Searching for common ground
Stikov, N., Boudreau, M., Levesque, I. R., Tardif, C. L., Barral, J. K., & Pike, G. B. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25135 · Cited by 285
Brain regions associated with retrieval of structurally coherent visual information
Schacter, D. L., Reiman, E. M., Uecker, A., Roister, M. R., Yun, L. S., & Cooper, L. A. (1995) · Nature · doi:10.1038/376587a0 · Cited by 285
Fiori, V., Coccia, M., Marinelli, C., Vecchi, V., Bonifazi, S., Ceravolo, M., Provinciali, L., Tomaiuolo, F., & Marangolo, P. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2010.21579 · Cited by 291
“The tDCS area was mapped onto the MNI standard space (Collins, Neelin, Peters, & Evans, 1994 ) using DISPLAY, an interactive program developed at the Montreal Neurological Institute by J.”
MethodDifferential brain responses to satiation in obese and lean men.
Gautier, J., Chen, K., Salbe, A. D., Bandy, D. J., Pratley, R. E., Heiman, M. L., Ravussin, É., Reiman, E. M., & Tataranni, P. A. (2000) · Diabetes · doi:10.2337/diabetes.49.5.838 · Cited by 283
“Automated algorithms were used to transform each subject’s brain MRI into standard atlas coordinates (27) and superimpose each Z-score map onto the averaged MRI to allow visual inspection of the composite images.”
MethodBehavioral and neural correlates of perceived and imagined musical timbre
Halpern, A. R., Zatorre, R. J., Bouffard, M. A., & Johnson, J. A. (2004) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2003.12.017 · Cited by 295
“ing a 6 mm gaussian kernel, corrected for motion, and transformed into standard stereotaxic space using in-house software ( Collins, Neelin, Peters, & Evans, 1994 ).”
MethodCerebral atrophy and its relation to cognitive impairment in Parkinson disease
Nagano‐Saito, A., Washimi, Y., Arahata, Y., Kachi, T., Lerch, J. P., Evans, A. C., Dagher, A., & Ito, K. (2005) · Neurology · doi:10.1212/01.wnl.0000149510.41793.50 · Cited by 284
Self-citationGhosh, S. S., Kakunoori, S., Augustinack, J., Nieto-Castanon, A., Kovelman, I., Gaab, N., Christodoulou, J. A., Triantafyllou, C., Gabrieli, J. D. E., & Fischl, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.075 · Cited by 281
Motor imagery after stroke: Relating outcome to motor network connectivity
Sharma, N., Baron, J., & Rowe, J. B. (2009) · Annals of Neurology · doi:10.1002/ana.21810 · Cited by 297
A large, open source dataset of stroke anatomical brain images and manual lesion segmentations
Liew, S.-L., Anglin, J. M., Banks, N. W., Sondag, M., Ito, K. L., Kim, H., Chan, J., Ito, J., Jung, C., Khoshab, N., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.11 · Cited by 283
Functional anatomical correlates of controlled and automatic processing
Jansma, J. M., Ramsey, N. F., Slagter, H. A., & Kahn, R. S. (2001) · Journal of Cognitive Neuroscience · doi:10.1162/08989290152541403 · Cited by 281
“A fourth contrast was created showing the signal differences between the NT (NT–RS contrast) and the CT (CT– RS), denoted as ‘‘NT–CT contrast.’’ Subsequently, in order to create group-average activity maps, all individual activity maps were spatially normalized onto a standard average brain using a linear transformati…”
MethodLerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H. J., & Evans, A. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.09.013 · Cited by 281
“The native MRI were registered into standardized stereotaxic space using a linear transformation [ 9 ].”
MethodSelf-citationAsymmetries of the planum temporale and Heschl's gyrus: relationship to language lateralization
Dorsaint-Pierre, R., Penhune, V. B., Watkins, K. E., Neelin, P., Lerch, J. P., Bouffard, M. A., & Zatorre, R. J. (2006) · Brain · doi:10.1093/brain/awl055 · Cited by 275
“The MR images were transformed into standardized MNI stereotaxic space (Talairach and Tournoux, 1988) using an automatic nine-parameter linear algorithm (Collins et al., 1994) based on the MNI 305 target.”
MethodSelf-citationLearning new sounds of speech: reallocation of neural substrates
Golestani, N., & Zatorre, R. J. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.071 · Cited by 273
“BOLD signal images were smoothed (6-mm FWHM), corrected for motion artifact and transformed into standard stereotaxic space (Talairach and Tournoux, 1988) using in-house software (Collins et al., 1994).”
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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