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Mitigating transmit‐B(1) artifacts by predicting parallel transmission images with deep learning: A feasibility study using high‐resolution whole‐brain diffusion at 7 Tesla
PURPOSE: To propose a novel deep learning (DL) approach to transmit‐B(1) (B(1) (+))‐artifact mitigation without direct use of parallel transmission (pTx), by predicting pTx images from single‐channel transmission (sTx) images. METHODS: A deep encoder–decoder convolutional neural network was construc...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324974/ https://www.ncbi.nlm.nih.gov/pubmed/35403237 http://dx.doi.org/10.1002/mrm.29238 |