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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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Kreifelts, B., Ethofer, T., Huberle, E., Grodd, W., & Wildgruber, D. (2010) · Human Brain Mapping · doi:10.1002/hbm.20913 · Cited by 132
“All functional images were resliced to a size of 3 3 3 mm3, normalized to the Montreal Neurological Institute (MNI) space [Collins et al., 1994], and smoothed using a Gaussian filter with 10-mm full width half maximum (FWHM) before statistical analysis.”
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“The native MRI scans are registered into standardized stereotaxic space using a 9 degrees of freedom linear transformation [Collins et al., 1994] and corrected for nonuniformity artifacts [Sled et al.”
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“Off-Line Data Analysis Statistical parametricmapping (SPM8)1 was conducted following standard procedures with realignment for motion correction, normalization into the MNI template space (Collins et al., 1994), and spatial smoothing with 8 mm full-width at half-maximum Gaussian kernel.”
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“Statistical Parametric Mapping (SPM8) was used for realignment and normalization to the Montreal Neurological Institute (MNI) coordinate system and spatial smoothing with a 6-mm full-width-at-half-maximum Gaussian kernel and global scaling (Collins et al., 1994).”
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“For each subject, each native T1w volume from each timepoint was linearly registered together to form a subject-specific template, aligned with the ICBM152 template (Collins et al., 1994; Evans and Collins, 1997).”
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“Images were de-noised [6], bias field corrected [33], registered to Montreal Neurological Institute (MNI) space [4] and skull stripped [9].”
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“Coordinates are in the space of the Montreal Neurological Institute (MNI; Collins et al. 1994) ASD within group analyses (correlation with self-injury scores) Brain region Side x y z P t Vertices Global Search Superior parietal lobule Right 11 -51 74 \0.0001 6.30 420 Directed search Primary…”
BackgroundMethodInfluentialMullen, K. T., Dumoulin, S. O., & Hess, R. F. (2008) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2008.06476.x · Cited by 62
“2. t-statistical maps of one subject (JS) showing a coronal (A and B) and axial view (C and D) of the left and right hemispheres in stereotaxic space (Talairach & Tournoux, 1988; Collins et al., 1994).”
BackgroundMethodResultInfluentialOrtiz, T., Poch, J., Santos, J. M., Requena, C., Martínez, A. M., Ortiz-Terán, L., Turrero, A., Barcia, J., Nogales, R., Calvo, A., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023264 · Cited by 63
“Different models were defined by constraining the source to one anatomic compartment, chosen using the probabilistic brain atlas (PBA) [22–23] and Brodmann atlas.”
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“The stereotaxic space was redefined (Evans et al., 1994) and data sets were linearly scaled to the original Talairach space (Collins et al., 1994, 1995).”
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Koh, S.-H., Choi, S. H., Jeong, J. H., Jang, J.-W., Park, K. W., Kim, E.-J., Kim, H. J., Hong, J. Y., Yoon, S. J., Yoon, B., et al. (2020) · Aging · doi:10.18632/aging.102893 · Cited by 51
“Corrected T1-weighted images in native space were aligned to the Montreal Neurological Institute 152 standard space [48].”
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Andrawis, J. P., Hwang, K. S., Green, A. E., Kotlerman, J., Elashoff, D., Morra, J. H., Cummings, J. L., Toga, A. W., Thompson, P. M., & Apostolova, L. G. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.07.020 · Cited by 54
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“2010), registration to a common space using a 12 parameter affine transformation (Collins et al. 1994), removal of non-head regions (such as the neck), and brain extraction using BEaST (Eskildsen et al.”
MethodAmphetamine-induced dopamine release and neurocognitive function in treatment-naive adults with ADHD
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“MR volumes were corrected for image intensity nonuniformity (Sled et al, 1998) and transformed into the Montreal Neurological Institute (MNI) space using automated feature matching to the MNI305 template (Collins et al, 1994).”
MethodAgam, Y., Greenberg, J. L., Isom, M., Falkenstein, M. J., Jenike, E., Wilhelm, S., & Manoach, D. S. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.06.002 · Cited by 46
“…surface-based coordinates back to the original structural volume for each of the individuals whose brains were used to create the template brain, registering the volumes to the Montreal Neurological Institute (MNI305) atlas (Collins et al., 1994) and averaging the corresponding MNI305 coordinates.”
Faridi, N., Karama, S., Burgaleta, M., White, M. T., Evans, A. C., Fonov, V., Collins, D. L., & Waber, D. P. (2015) · Neuropsychology · doi:10.1037/neu0000079 · Cited by 42
Self-citationA comparison of accurate automatic hippocampal segmentation methods
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Self-citationLi, W., Kezele, I., Collins, D. L., Zijdenbos, A., Keyak, J., Kornak, J., Koyama, A., Saeed, I., LeBlanc, A., Harris, T., et al. (2007) · Bone · doi:10.1016/j.bone.2007.07.006 · Cited by 37
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“After manual transfer to the normal template space, the template brain was warped to the MNI152 standard 1 mm T1-weighted atlas (Collins et al., 1994; Evans et al., 1991; Mazziotta et al., 2001) using BRAINSDemonWarp (Johnson and Zhao, 2009).”
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“The MR images were normalized to the Montreal Neurological Institute (MNI) space [42] using a transformation matrix that was calculated on the basis of the structural T1-weighted 3-D data set of each participant and subsequently applied to the functional images (resampled voxel size: 36363 mm(3)).”
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“Preprocessing steps included a nonuniformity correction (Sled et al. 1998), skull stripping (Smith 2002) and alignment to the common image space of the Montreal Neurological Institute (Collins et al. 1994).”
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“Both the head model and the electrode coordinates are co-registered to the Talairach brain atlas (Talairach and Tournoux,1988), digitized at the Brain Imaging Center of theMontreal Neurological Institute (Collins et al., 1994).”
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“The native MRI scans were registered into standardized stereotaxic space and were corrected for nonuniformity artifacts (Sled et al., 1998) using a linear transformation (Collins et al., 1994).”
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“This could be a limitation of the study although this method is validated and used as default setting in Freesurfer analysis [59].”
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“Each native T1w volume from each timepoint was linearly registered first to the subject-specific template which was then registered to the ICBM152 template.(32) All images were then non-linearly registered to the ICBM152 template using ANTs diffeomorphic registration pipeline.”
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“Preprocessing of the data encompassed motion correction, normalization into the Montreal Neurological Institute template space [15] and smoothing with a 12 mm full-width half-maximum Gaussian kernel.”
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“(iii) Co-registration of the functional and anatomical images; (iv) normalization of the T1 image to the MNI305 template image (Collins et al., 1994).”
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“All images underwent automated correction for intensity non-uniformity and intensity standardization, automatic registration in a stereotactic space [MNI-Collins et al., 1994] and were automatically skull stripped.”
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“The specific brain areas where the observed BOLD changes occur are reported using the Montreal Neurological Institute [11], MNI).”
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“to a single template [1–4].”
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High-resolution intersubject averaging and a coordinate system for the cortical surface
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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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