Research on low-dose CT image enhancement algorithm based on deep learning

Low-dose CT has become a commonly used means of CT examination in clinical practice because of its relatively small amount of radiation and less radiation damage to the human body. However, its noise and image quality are lower than that of conventional CT and high-resolution CT, which directly affects the accuracy of diagnosis. The study of low-dose CT image enhancement technology has always been a research hotspot. In this paper, we analyze the current research status of supervised and unsupervised learning in low-dose CT image enhancement technology with deep learning methods. A hybrid network of CycleGAN and Transformer is proposed, and ablation experiments are conducted to verify the effectiveness of the proposed algorithm, which provides a useful reference for researchers and clinicians in this field.