Fully Convolutional Architecture for Low-Dose CT Image Noise Reduction

[摘要] One of the critical topics in medical low-dose Computed Tomography (CT) imaging is how best to maintain image quality. As the quality of images decreases with lowering the X-ray radiation dose, improving image quality is extremely important and challenging. We have proposed a novel approach to denoise low-dose CT images. Our algorithm learns directly from an end-to-end mapping from the low-dose Computed Tomography images for denoising the normal-dose CT images. Our method is based on a deep convolutional neural network with rectified linear units. By learning various low-level to high-level features from a low-dose image the proposed algorithm is capable of creating a high-quality denoised image. We demonstrate the superiority of our technique by comparing the results with two other state-of-the-art methods in terms of the peak signal to noise ratio, root mean square error, and a structural similarity index.

[发布机构] Department of Electrical and Computer Engineering, Ryerson University, Toronto; ON; M5B 2K3, Canada^1;Department of AI, Faculty of Computer Engineering, University of Isfahan, Isfahan; 81746, Iran^2;Department of Medical Imaging, University of Saskatoon, Saskatoon; SK; S7N 0W8, Canada^3

[学科分类] 计算机科学（综合）

[关键词] Deep convolutional neural networks;High-level features;Linear units;Peak signal to noise ratio;Root mean square errors;State-of-the-art methods;Structural similarity indices;X ray radiation