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Predicting (15)O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias
To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- and multi-delay arterial spin labeling (ASL) and structural images to predict gold-standard (15)O-water PET CBF images obtained on a simultaneous PET/...
Guardat en:
| Publicat a: | J Cereb Blood Flow Metab |
|---|---|
| Autors principals: | , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
SAGE Publications
2019
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7585922/ https://ncbi.nlm.nih.gov/pubmed/31722599 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1177/0271678X19888123 |
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