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Generation of PET Attenuation Map for Whole-Body Time-of-Flight (18)F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps
We propose a new deep learning–based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algor...
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| Publicado no: | J Nucl Med |
|---|---|
| Main Authors: | , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Society of Nuclear Medicine
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6681691/ https://ncbi.nlm.nih.gov/pubmed/30683763 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2967/jnumed.118.219493 |
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