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The Computed Tomography (CT) for diagnosis of lesions in human internalorgans 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 onmodel-based methods or deep-learning-based techniques, which often ignore thecoherence and smoothness for sequential CT slices. To address this issue, wepropose a novel approach using generative adversarial networks (GANs) withenhanced local coherence. The proposed method can capture the local coherenceof adjacent images by optical flow, which yields significant improvements inthe precision and stability of the constructed images. We evaluate our proposedmethod on real datasets and the experimental results suggest that it canoutperform existing state-of-the-art reconstruction approaches significantly.

Solving Low-Dose CT Reconstruction via GAN with Local Coherence