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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Bibliografiset tiedot
Julkaisussa:Phys Med Biol
Päätekijät: Dong, Xue, Wang, Tonghe, Lei, Yang, Higgins, Kristin, Liu, Tian, Curran, Walter J, Mao, Hui, Nye, Jonathon A, Yang, Xiaofeng
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: 2019
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Article
Linkit: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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