Half2Half: deep neural network based CT image denoising without independent reference data

Abstract

Reducing radiation dose of x-ray computed tomography (CT) and thereby decreasing the potential risk to patients are desirable in CT imaging. Deep neural network (DNN) has been proposed to reduce noise in low-dose CT (LdCT) images and showed promising results. However, most existing DNN-based methods require training a neural network using high-quality CT images as the reference. Lack of high-quality reference data has therefore been the bottleneck in the current DNN-based methods. Recently, a noise-to-noise (Noise2Noise) training method was proposed to train a denoising neural network with only noisy images. It has also been applied to LdCT data in both the count domain and image domain. However, the method still requires a separately acquired independent noisy reference image for supervising the training procedure. To address this limitation, we propose a novel method to generate both training inputs and training labels from the existing CT scans, which does not require any additional high-dose CT images or repeated scans. Therefore, existing large noisy dataset can be fully exploited for training a denoising neural network. Our experimental results show that the trained networks can reduce noise in existing CT image and hence improve the image quality for clinical diagnosis.