Transfer-GAN: Multimodal CT Image Super-Resolution Via Transfer Generative Adversarial Networks

Multimodal CT scans, including non-contrast CT, CT perfusion, and CT angiography, are widely used in acute stroke diagnosis and therapeutic planning. While each imaging modality has its advantage in brain cross-sectional feature visualizations, the varying image resolution of different modalities hinders the ability of the radiologist to discern consistent but subtle suspicious findings. Besides, higher image quality requires a high radiation dose, leading to increases in health risks such as cataract formation and cancer induction. In this work, we propose a deep learning-based method Transfer-GAN that utilizes generative adversarial networks and transfer learning to improve multimodal CT image resolution and to lower the necessary radiation exposure. Through extensive experiments, we demonstrate that transfer learning from multimodal CT provides substantial visualization and quantity enhancement compare to the training without learning the prior knowledge.