Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis.

Chenggong Yan Jie Lin Haixia Li Jun Xu Tianjing Zhang Hao Chen Henry C Woodruff Guangyao Wu Siqi Zhang Yikai Xu Philippe Lambin

Published in: Korean journal of radiology (2021)

The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

Keyphrases

magnetic resonance
pulmonary tuberculosis
quality improvement
mycobacterium tuberculosis
deep learning
computed tomography
image quality
convolutional neural network
patient safety
dual energy
optical coherence tomography
magnetic resonance imaging
positron emission tomography
machine learning