Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss

The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists’ judgment and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio is improved, MSE- or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

I. Introduction

X-ray computed tomography (CT) is one of the most important imaging modalities in modern hospitals and clinics. However, there is a potential radiation risk to the patient, since x-rays could cause genetic damage and induce cancer in a probability related to the radiation dose [1], [2]. Lowering the radiation dose increases the noise and artifacts in reconstructed images, which can compromise diagnostic information. Hence, extensive efforts have been made to design better image reconstruction or image processing methods for low-dose CT (LDCT). These methods generally fall into three categories: (a) sinogram filtration before reconstruction [3]–[5], (b) iterative reconstruction [6], [7], and (c) image post-processing after reconstruction [8]–[10].