Sharpness-aware Low dose CT denoising using conditional generative adversarial network

TL;DR

This paper introduces a deep learning approach using a conditional GAN and sharpness detection to improve low dose CT denoising, reducing noise while preserving image resolution better than existing methods.

Contribution

The paper proposes a novel sharpness-aware GAN framework with a sharpness detection network for LDCT denoising, addressing blur issues in high noise scenarios.

Findings

Achieves superior denoising performance compared to state-of-the-art methods.

Maintains high resolution with minimal blur in reconstructed images.

Effective on both simulated and real datasets.

Abstract

Low Dose Computed Tomography (LDCT) has offered tremendous benefits in radiation restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset shows that the results of the proposed method have very small resolution loss and achieves better performance relative to the-state-of-art methods both quantitatively and visually.