SRflow: Deep learning based super-resolution of 4D-flow MRI data

Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propo...

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Detalles Bibliográficos
Autores principales: Shit, Suprosanna, Zimmermann, Judith, Ezhov, Ivan, Paetzold, Johannes C., Sanches, Augusto F., Pirkl, Carolin, Menze, Bjoern H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411720/
https://www.ncbi.nlm.nih.gov/pubmed/36034591
http://dx.doi.org/10.3389/frai.2022.928181

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411720/
https://www.ncbi.nlm.nih.gov/pubmed/36034591
http://dx.doi.org/10.3389/frai.2022.928181

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