CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging
Abstract
Although lowering X-ray radiation helps reduce the risk of cancer in patients, low-dose computed tomography (LDCT) technology usually leads to poor image quality, such as amplified mottle noise and streak artifacts, which severely impact the diagnostic results. To improve diagnostic performance, we propose an algorithm based on a cycle-consistent generative adversarial network (CycleGAN) to suppress noise and reduce artifacts. In addition, we...
Paper Details
Title
CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging
Published Date
Jan 1, 2020
Volume
6
Pages
1203 - 1218
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