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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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| Publicat a: | Phys Med Biol |
|---|---|
| Autors principals: | , , , , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2019
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| Matèries: | |
| Accés en línia: | 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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