Ring artifact removal for differential phase-contrast X-ray computed tomography using a conditional generative adversarial network.
Zhuoran HuangNaoki SunaguchiDaisuke ShimaoAtsushi EnomotoShu IchiharaTetsuya YuasaMasami AndoPublished in: International journal of computer assisted radiology and surgery (2021)
We proposed a cGAN-based method for RA removal that exploits the physical properties of d-PCCT. The proposed method was able to completely remove RA from d-PCCT images on both simulated data and biological data. We believe that this method is useful for the observation of various types of biological soft tissue.
Keyphrases
- computed tomography
- dual energy
- rheumatoid arthritis
- electronic health record
- soft tissue
- big data
- disease activity
- magnetic resonance
- deep learning
- high resolution
- contrast enhanced
- magnetic resonance imaging
- mental health
- positron emission tomography
- image quality
- machine learning
- artificial intelligence
- mass spectrometry
- systemic lupus erythematosus