Enabling machine learning in X-ray-based procedures via realistic simulation of image formation.
Jie Ying WuJan-Nico ZaechCong GaoBastian BierFlorian GoldmannSing Chun LeeJavad FotouhiRussell TaylorMehran ArmandNassir NavabPublished in: International journal of computer assisted radiology and surgery (2019)
Our findings for both tasks are positive and promising. Combined with complementary approaches, such as image style transfer, the proposed framework for fast and realistic simulation of fluoroscopy from CT contributes to promoting the implementation of machine learning in X-ray-guided procedures. This paradigm shift has the potential to revolutionize intra-operative image analysis to simplify surgical workflows.
Keyphrases
- machine learning
- dual energy
- deep learning
- computed tomography
- artificial intelligence
- high resolution
- image quality
- big data
- virtual reality
- primary care
- healthcare
- working memory
- contrast enhanced
- quality improvement
- magnetic resonance imaging
- electron microscopy
- positron emission tomography
- risk assessment
- human health
- magnetic resonance
- mass spectrometry
- atrial fibrillation