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

Half2Half: deep neural network based CT image denoising without independent reference data.
Phys Med Biol. 2020 Jul 24;:
Authors: Yuan N, Zhou J, Qi J
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 images and showed promising results. However, most existing DNN-based methods require training a neural network using high-quality CT images as a 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 low-dose CT 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 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.
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Source: Physics in Medicine and Biology - July 23, 2020 Category: Physics Authors: Yuan N, Zhou J, Qi J Tags: Phys Med Biol Source Type: research