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MRI-PET Registration with Automated Algorithm
Abstract
OBJECTIVE: We have previously reported an automated method for within-modality (e.g., PET-to-PET) image alignment. We now describe modifications to this method that allow for cross-modality registration of MRI and PET brain images obtained from a single subject. METHODS: This method does not require fiducial markers and the user is not required to identify common structures on the two image sets. To align the images, the algorithm seeks to minimize the standard deviation of the PET pixel values that correspond to each MRI pixel value. The MR images must be edited to exclude nonbrain regions prior to using the algorithm. RESULTS AND CONCLUSION: The method has been validated quantitatively using data from patients with stereotaxic fiducial markers rigidly fixed in the skull. Maximal three-dimensional errors of < 3 mm and mean three-dimensional errors of < 2 mm were measured. Computation time on a SPARCstation IPX varies from 3 to 9 min to align MR image sets with [18F]fluorodeoxyglucose PET images. The MR alignment with noisy H2(15)O PET images typically requires 20-30 min.
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Lee, K. H., Oppenheimer, C. W., Siegle, G. J., Ladouceur, C. D., Lee, G. E., Silk, J. S., & Dahl, R. E. (2018) · Journal of Research on Adolescence · doi:10.1111/jora.12360 · Cited by 5
Dopamine Type 2/3 Receptor Availability in the Striatum and Social Status in Human Volunteers
Martinez, D., Orlowska, D., Narendran, R., Slifstein, M., Liu, F., Kumar, D., Broft, A., Van Heertum, R., & Kleber, H. D. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.07.037 · Cited by 146
“, Sterling, Virginia) as previously published (11,13).”
Metadata
- Authors
- Woods, R. P., Mazziotta, J. C., & R. Cherry, A. S.
- Venue
- Journal of Computer Assisted Tomography
- Published
- 1993-07
- Volume
- 17
- Issue
- 4
- Pages
- 536-546
- ISSN
- 0363-8715 (ISSN-L)
- OpenAlex
- W2051809205
- Cited by
- 1,617
- References
- 0
- FWCI
- 62.52
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
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.
