Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks

TL;DR

This paper introduces an unsupervised deep learning framework for denoising low-dose liver CT scans, combining U-Net, attention mechanisms, and perceptual loss, validated on real clinical data with expert evaluation.

Contribution

It presents a novel unsupervised denoising method that effectively handles real clinical low-dose CT data without requiring paired training samples.

Findings

Outperforms classical denoising methods on real clinical data.

Achieves high-quality denoising validated by medical professionals.

Addresses the challenge of unsupervised learning with real-world medical images.

Abstract

With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using deep learning. Although low-dose computed tomography reduces radiation exposure to patients, it also introduces more noise, which may interfere with visual interpretation by physicians and affect diagnostic results. To address this problem, inspired by Cycle-GAN for unsupervised learning, this paper proposes an end-to-end unsupervised low-dose computed tomography denoising framework. The proposed framework combines a U-Net structure for multi-scale feature extraction, an attention mechanism for feature fusion, and a residual network for feature transformation. It also introduces perceptual loss to improve the network for the characteristics of medical images. In addition, we construct a real low-dose computed tomography dataset and design a large number of comparative experiments to validate the proposed method, using both image-based evaluation metrics and medical evaluation criteria. Compared with classical methods, the main advantage of this paper is that it addresses the limitation that real clinical data cannot be directly used for supervised learning, while still achieving excellent performance. The experimental results are also professionally evaluated by imaging physicians and meet clinical needs.