NAP-CycleGAN: A New Cyclegan-Based CT Images Synthesis Model For Clinical Image Reconstruction Using Brain MR Images

Abstract

The intricate structure of the brain often necessitates the combined use of magnetic resonance (MR) and computed tomography (CT) imaging for comprehensive diagnostics in clinical care. However, certain patients cannot be exposed to radiation-intensive CT scans, leading to data scarcity and affecting subsequent treatment. In this regard, this paper proposes a new model NAP-CycleGAN, replacing the generator with pix2pixHD utilizing multi-scale strategies and context-aware modules. By integrating channel attention, the model effectively extracts relevant image features, allowing adaptive weight assignment and handling of long-range dependencies. Additionally, Gaussian noise is introduced to the discriminator to counteract adversarial sample attacks and prevent gradient vanishing. Furthermore, feature matching loss and cycle consistency loss are integrated to reduce image detail distortion. To verify the model validity, it is compared with NICEGAN, MR to CT, DualGAN, CGAN, CycleGAN, pix2pix, and VAE. The experimental results show that the proposed model outperforms the seven methods, it yields the best, and the synthetic CTs of the proposed model are closest to the original CT (RCT) images.