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Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging
Attenuation correction (AC) of PET/MRI faces challenges including inter-scan motion, image artifacts such as truncation and distortion, and erroneous transformation of structural voxel-intensities to PET mu-map values. We propose a deep-learning-based method to derive synthetic CT (sCT) images from...
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| Pubblicato in: | Phys Med Biol |
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| Autori principali: | , , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7759014/ https://ncbi.nlm.nih.gov/pubmed/31622962 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1088/1361-6560/ab4eb7 |
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