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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

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