MR-contrast-aware image-to-image translations with generative adversarial networks.
Jonas DenckJens GuehringAndreas MaierEva RothgangPublished in: International journal of computer assisted radiology and surgery (2021)
Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.
Keyphrases
- contrast enhanced
- deep learning
- magnetic resonance imaging
- magnetic resonance
- computed tomography
- diffusion weighted imaging
- healthcare
- high resolution
- machine learning
- convolutional neural network
- working memory
- air pollution
- dual energy
- mass spectrometry
- image quality
- photodynamic therapy
- fluorescence imaging
- soft tissue
- virtual reality