Solving Low-Dose CT Reconstruction via GAN with Local Coherence
Wenjie Liu
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.