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

TL;DR

This paper presents a novel CT image denoising method using a GAN with Wasserstein distance and perceptual loss, effectively reducing noise while preserving important image details in clinical CT scans.

Contribution

Introduces a new GAN-based CT denoising approach combining Wasserstein distance and perceptual loss for improved noise reduction and detail preservation.

Findings

Effective noise reduction in clinical CT images

Preserves critical image details during denoising

Outperforms traditional methods in quality metrics

Abstract

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.