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Rapid Automated Algorithm for Aligning and Reslicing PET Images
Abstract
A computer algorithm for the three-dimensional (3D) alignment of PET images is described. To align two images, the algorithm calculates the ratio of one image to the other on a voxel-by-voxel basis and then iteratively moves the images relative to one another to minimize the variance of this ratio across voxels. Since the method relies on anatomic information in the images rather than on external fiducial markers, it can be applied retrospectively. Validation studies using a 3D brain phantom show that the algorithm aligns images acquired at a wide variety of positions with maximum positional errors that are usually less than the width of a voxel (1.745 mm). Simulated cortical activation sites do not interfere with alignment. Global errors in quantitation from realignment are less than 2%. Regional errors due to partial volume effects are largest when the gantry is rotated by large angles or when the bed is translated axially by one-half the interplane distance. To minimize such partial volume effects, the algorithm can be used prospectively, during acquisition, to reposition the scanner gantry and bed to match an earlier study. Computation requires 3-6 min on a Sun SPARCstation 2.
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Hemispheric lateralization of somatosensory processing
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Neural substrates of decision making in adults with attention deficit hyperactivity disorder
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Motivation-dependent responses in the human caudate nucleus.
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Regional Differences in the CBF and BOLD Responses to Hypercapnia: A Combined PET and fMRI Study
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The neural basis of visual skill learning: an fMRI study of mirror reading
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Metadata
- Authors
- Woods, R. P., Cherry, S. R., & Mazziotta, J. C.
- Venue
- Journal of Computer Assisted Tomography
- Published
- 1992-07
- Volume
- 16
- Issue
- 4
- Pages
- 620-633
- ISSN
- 0363-8715 (ISSN-L)
- OpenAlex
- W1990005524
- Cited by
- 1,912
- References
- 0
- FWCI
- 32.32
- 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.