Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
This addresses the problem of image quality in medical imaging for clinicians, but it appears incremental as it builds on existing GAN techniques with specific modifications.
The paper tackled low-dose CT image denoising by proposing a GAN-based method using Wasserstein distance and perceptual loss, achieving promising results in reducing noise while preserving critical information in clinical CT images.
In this paper, we introduce 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 transform theory, and promises to improve the performance of the GAN. The perceptual loss compares the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN helps migrate the data noise distribution from strong to weak. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task, is capable of not only reducing the image noise level but also keeping the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.