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Cited by 572

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A Stereotactic Method of Anatomical Localization for Positron Emission Tomography

Fox, Peter T., Perlmutter, Joel S., Raichle, Marcus E.·1985·Journal of Computer Assisted Tomography

Abstract

Extant methods for anatomical localization within a physiological image are inadequate for precise regional correlations between anatomy and physiology. We have developed a method for determining the location of any region of interest within a positron emission tomographic (PET) image by transformation of the tomographic location coordinates into coordinates within a stereotactic atlas of the human brain. The specific anatomical assumptions of this approach were identified and validated within groups of normal subjects by means of measurements made from lateral skull radiographs and nuclear magnetic resonance proton images. A high degree of accuracy and reproducibility was demonstrated when this technique was applied to groups of normal subjects by determining the anatomical location of two physiologically defined PET areas: the cavernous sinus and visually responsive cortex. This method is simple to implement and highly objective. Generalization of the localization procedure for use with structural tomography is readily accomplished.

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Metadata

Authors
Fox, P. T., Perlmutter, J. S., & Raichle, M. E.
Venue
Journal of Computer Assisted Tomography
Published
1985-01
Volume
9
Issue
1
Pages
141-153
ISSN
0363-8715 (ISSN-L)
OpenAlex
W2047556230
Cited by
572
References
0
FWCI
22.70
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Stereotactic Method of Anatomical Localization for Positron Emission Tomography. https://app.cochat.ai/papers/10.1097/00004728-198501000-00025
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Fox, P. T., Perlmutter, J. S., & Raichle, M. E. (1985). A Stereotactic Method of Anatomical Localization for Positron Emission Tomography. Journal of Computer Assisted Tomography. https://doi.org/10.1097/00004728-198501000-00025

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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.