Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

Abstract Background To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction images. Methods Retrospective, ethically approved stu...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Gustav Müller-Franzes, Luisa Huck, Maike Bode, Sven Nebelung, Christiane Kuhl, Daniel Truhn, Teresa Lemainque
Format: Artykuł
Język:English
Wydane: SpringerOpen 2024-05-01
Seria:European Radiology Experimental
Hasła przedmiotowe:
Artificial intelligence
Breast neoplasms
Contrast media
Machine learning
Magnetic resonance imaging
Dostęp online:https://doi.org/10.1186/s41747-024-00451-3

Internet

https://doi.org/10.1186/s41747-024-00451-3

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