Synthesis of CT images from digital body phantoms using CycleGAN.

Tom RussStephan GoerttlerAlena-Kathrin SchnurrDominik F BauerSepideh HatamikiaLothar R SchadFrank G ZöllnerKhanlian Chung

Published in: International journal of computer assisted radiology and surgery (2019)

CT synthesis using CycleGAN is a feasible approach to generate realistic images from simulated XCAT phantoms. Synthetic CTs generated with a task-based loss function can be used in addition to real data to improve the performance of segmentation networks.

Keyphrases

deep learning
convolutional neural network
image quality
dual energy
computed tomography
contrast enhanced
optical coherence tomography
positron emission tomography
artificial intelligence
electronic health record
magnetic resonance imaging
big data
machine learning
magnetic resonance