DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis

H Li, JC Paetzold, A Sekuboyina, F Kofler… - … Image Computing and …, 2019 - Springer
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single
sequences are often missing or are of poor quality (eg due to motion). Naturally, the idea …
被引用次数：97 相关文章

DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, JC Paetzold, A Sekuboyina, F Kofler… - arXiv e …, 2019 - ui.adsabs.harvard.edu
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single
sequences are often missing or are of poor quality (eg due to motion). Naturally, the idea …

DiamondGan: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, J Paetzold, A Sekuboyina, F Kofler, J Zhang… - 2019 - mediatum.ub.tum.de
Synthesizing MR imaging sequences is highly attractive for clinical practice, as often single
sequences are missing or of poor quality (eg due to motion). Naturally, the idea arises that a …

DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, JC Paetzold, A Sekuboyina, F Kofler… - … Conference on Medical …, 2019 - dl.acm.org
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single
sequences are often missing or are of poor quality (eg due to motion). Naturally, the idea …
[PDF] tum.de

[PDF][PDF] DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, JC Paetzold, A Sekuboyina, F Kofler… - arXiv preprint arXiv …, 2019 - campar.in.tum.de
Synthesizing MR imaging sequences is highly attractive for clinical practice, as often single
sequences are missing or of poor quality (eg due to motion). Naturally, the idea arises that a …
[PDF] researchgate.net

[PDF][PDF] DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, JC Paetzold, A Sekuboyina, F Kofler… - arXiv preprint arXiv …, 2019 - researchgate.net
Recent studies on medical image synthesis reported promising results using generative
adversarial networks on single modalities. Naturally, the idea arises that a target modality …

DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, JC Paetzold, A Sekuboyina, F Kofler… - arXiv preprint arXiv …, 2019 - arxiv.org
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single
sequences are often missing or are of poor quality (eg due to motion). Naturally, the idea …

[引用][C] DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis

H Li, JC Paetzold, A Sekuboyina… - Lecture Notes in …, 2019 - mediatum.ub.tum.de
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