MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation

BACKGROUND: Standardised performance assessment of image acquisition, reconstruction and processing methods is limited by the absence of images paired with ground truth reference values. To this end, we propose MRXCAT2.0 to generate synthetic data, covering healthy and pathological function, using a...

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Detalles Bibliográficos
Autores principales: Buoso, Stefano, Joyce, Thomas, Schulthess, Nico, Kozerke, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116689/
https://www.ncbi.nlm.nih.gov/pubmed/37076840
http://dx.doi.org/10.1186/s12968-023-00934-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116689/
https://www.ncbi.nlm.nih.gov/pubmed/37076840
http://dx.doi.org/10.1186/s12968-023-00934-z

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