Solving Low-Dose CT Reconstruction via GAN with Local Coherence

TL;DR

This paper introduces a GAN-based method for low-dose CT reconstruction that leverages local coherence between slices via optical flow, significantly improving image quality and stability.

Contribution

It presents a novel GAN framework incorporating optical flow to enhance local coherence in sequential CT slices, addressing a key limitation of prior methods.

Findings

Outperforms existing state-of-the-art methods in image quality.

Improves stability and coherence of reconstructed CT slices.

Validated on real datasets with significant gains.

Abstract

The Computed Tomography (CT) for diagnosis of lesions in human internal organs is one of the most fundamental topics in medical imaging. Low-dose CT, which offers reduced radiation exposure, is preferred over standard-dose CT, and therefore its reconstruction approaches have been extensively studied. However, current low-dose CT reconstruction techniques mainly rely on model-based methods or deep-learning-based techniques, which often ignore the coherence and smoothness for sequential CT slices. To address this issue, we propose a novel approach using generative adversarial networks (GANs) with enhanced local coherence. The proposed method can capture the local coherence of adjacent images by optical flow, which yields significant improvements in the precision and stability of the constructed images. We evaluate our proposed method on real datasets and the experimental results suggest that it can outperform existing state-of-the-art reconstruction approaches significantly.