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Deep learning-based Attenuation Correction in the Absence of Structural Information for Whole-body PET Imaging
Deriving accurate structural maps for attenuation correction (AC) of whole-body PET remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensities to PET μ-map values (e.g. modality artifacts, implanted devices, or contrast ag...
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| Pubblicato in: | Phys Med Biol |
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| Autori principali: | , , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7099429/ https://ncbi.nlm.nih.gov/pubmed/31869826 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1088/1361-6560/ab652c |
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