Low-dose CT denoising with convolutional neural network
This addresses the need for safer medical imaging by reducing radiation risk in CT scans, though it appears incremental as it builds on existing deep learning methods for image denoising.
The paper tackles the problem of image quality deterioration in low-dose CT by proposing a deep convolutional neural network that transforms low-dose images to normal-dose images patch by patch, achieving competitive performance as demonstrated through visual and quantitative evaluation.
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images towards normal-dose CT images, patch by patch. Visual and quantitative evaluation demonstrates a competing performance of the proposed method.