Cargando…
Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks
Multi-contrast MRI images use different echo and repetition times to highlight different tissues. However, not all desired image contrasts may be available due to scan-time limitations, suboptimal signal-to-noise ratio, and/or image artifacts. Deep learning approaches have brought revolutionary adva...
Autores principales: | Zhang, Huixian, Li, Hailong, Dillman, Jonathan R., Parikh, Nehal A., He, Lili |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026507/ https://www.ncbi.nlm.nih.gov/pubmed/35453864 http://dx.doi.org/10.3390/diagnostics12040816 |
Ejemplares similares
-
Multi-Level Cycle-Consistent Adversarial Networks with Attention Mechanism for Face Sketch-Photo Synthesis
por: Ren, Danping, et al.
Publicado: (2022) -
Lund jet images from generative and cycle-consistent adversarial networks
por: Dreyer, Frederic Alexandre
Publicado: (2019) -
Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19
por: Kalantar, Reza, et al.
Publicado: (2023) -
A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants
por: Li, Zhiyuan, et al.
Publicado: (2022) -
A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants
por: He, Lili, et al.
Publicado: (2020)