图像噪声
图像质量
中值滤波器
核医学
双边滤波器
辐射剂量
作者
Nimu Yuan,Jian Zhou,Jinyi Qi
摘要
Reducing radiation dose of computed tomography (CT) and thereby decreasing the potential risk to patients are desirable in CT imaging. Deep neural network has been proposed to reduce noise in low-dose CT images. However, the conventional way to train a neural network requires using high-dose CT images as the reference. Recently, a noise-tonoise (N2N) training method was proposed, which showed that a neural network could be trained with only noisy images. In this work, we applied the N2N training to low-dose CT denoising. Our results show that the N2N training works in both count and image domains without using any high-dose reference images.
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