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Spatial registration and normalization of images
Abstract
This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time‐series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley‐Liss, Inc.
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Büchel, C., Bornhövd, K., Quante, M., Glauche, V., Bromm, B., & Weiller, C. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-03-00970.2002 · Cited by 380
Linguistic threat activates the human amygdala
Isenberg, N., Silbersweig, D., Engelien, A., Emmerich, S., Malavade, K., Beattie, B., Leon, A. C., & Stern, E. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10456 · Cited by 378
Metadata
- Authors
- Friston, K. J., Ashburner, J., Frith, C. D., Poline, J. ‐., Heather, J. D., & Frackowiak, R. S. J.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 1995-01
- Volume
- 3
- Issue
- 3
- Pages
- 165-189
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- OpenAlex
- W2063237661
- Cited by
- 3,744
- References
- 1
- FWCI
- 109.16
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.