Deep learning‐based reconstruction of in vivo pelvis conductivity with a 3D patch‐based convolutional neural network trained on simulated MR data

PURPOSE: To demonstrate that mapping pelvis conductivity at 3T with deep learning (DL) is feasible. METHODS: 210 dielectric pelvic models were generated based on CT scans of 42 cervical cancer patients. For all dielectric models, electromagnetic and MR simulations with realistic accuracy and precisi...

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Detalles Bibliográficos
Autores principales: Gavazzi, Soraya, van den Berg, Cornelis A. T., Savenije, Mark H. F., Kok, H. Petra, de Boer, Peter, Stalpers, Lukas J. A., Lagendijk, Jan J. W., Crezee, Hans, van Lier, Astrid L. H. M. W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Full Papers—Computer Processing and Modeling
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402024/
https://www.ncbi.nlm.nih.gov/pubmed/32314825
http://dx.doi.org/10.1002/mrm.28285

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402024/
https://www.ncbi.nlm.nih.gov/pubmed/32314825
http://dx.doi.org/10.1002/mrm.28285

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