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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/...

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Dades bibliogràfiques
Publicat a:J Cereb Blood Flow Metab
Autors principals: Guo, Jia, Gong, Enhao, Fan, Audrey P, Goubran, Maged, Khalighi, Mohammad M, Zaharchuk, Greg
Format: Artigo
Idioma:Inglês
Publicat: SAGE Publications 2019
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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