IEEE Transactions on Medical Imaging· 2018Q1
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
- 1,628citations
- Q1SCImago
- 2018year
Short summary
A new generative adversarial network (GAN) method using Wasserstein distance and perceptual loss reduces noise in low-dose CT scans while preserving critical structural details, outperforming traditional MSE-based methods.
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Key points
- Introduces a GAN-based denoising method for low-dose CT images.
- Incorporates Wasserstein distance for improved GAN performance.
- Employs perceptual loss to preserve structural details by comparing feature spaces.
- Outperforms MSE-based methods by reducing noise while maintaining critical information.
- Demonstrates promising results on clinical CT images.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. IEEE Transactions on Medical Imaging, 2018 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine