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Solving Low-Dose CT Reconstruction via GAN with Local Coherence

The Computed Tomography (CT) for diagnosis of lesions in human internal organs is one of the most fundamental topics in medical imaging. Low-dose CT, which offers reduced radiation exposure, is preferred over standard-dose CT, and therefore its reconstruction approaches have been extensively studied. However, current low-dose CT reconstruction techniques mainly rely on model-based methods or deep-learning-based techniques, which often ignore the coherence and smoothness for sequential CT slices. To address this issue, we propose a novel approach using generative adversarial networks (GANs) with enhanced local coherence. The proposed method can capture the local coherence of adjacent images by optical flow, which yields significant improvements in the precision and stability of the constructed images. We evaluate our proposed method on real datasets and the experimental results suggest that it can outperform existing state-of-the-art reconstruction approaches significantly.

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