Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network.
Purpose: The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected (NAC) PET images for both rest and stress-state static or dynamic [13N]ammonia MP PET based on a generative adversarial network. Methods: We recruit...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 50; no. 12; pp. 3630 - 3647 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |