Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation
- DOI
- 10.1088/1361-6560/ae8c9f
- Published
- 2026-08-04
- Container
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1088/1361-6560/ae8c9f,
title = {Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation},
author = {Lydia Lim and David Little and Stewart Redman and Andrew Cookson},
year = {2026},
journal = {Physics in Medicine \& Biology},
doi = {10.1088/1361-6560/ae8c9f},
url = {https://doi.org/10.1088/1361-6560/ae8c9f}
}RIS
TY - JOUR TI - Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation AU - Lydia Lim AU - David Little AU - Stewart Redman AU - Andrew Cookson PY - 2026 JO - Physics in Medicine & Biology DO - 10.1088/1361-6560/ae8c9f UR - https://doi.org/10.1088/1361-6560/ae8c9f ER -
APA
Lim, L., Little, D., Redman, S., & Cookson, A. (2026). Machine learning for predicting full-count FDG PET brain images from low-count acquisitions in suspected dementia: a clinical and quantitative evaluation. Physics in Medicine & Biology. https://doi.org/10.1088/1361-6560/ae8c9f
Source records
- crossref · retrieved 2026-09-25T13:17:20.721Z