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A deep learning approach for (18)F-FDG PET attenuation correction
BACKGROUND: To develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was developed utilizing deep learning to generate continuously va...
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| Pubblicato in: | EJNMMI Phys |
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| Autori principali: | , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer International Publishing
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6230542/ https://ncbi.nlm.nih.gov/pubmed/30417316 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s40658-018-0225-8 |
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