Medical Image Synthesis with Context-Aware Generative Adversarial Networks

TL;DR

This paper introduces a context-aware generative adversarial network that synthesizes CT images from MRI scans, improving accuracy and realism for medical applications like radiotherapy planning.

Contribution

The paper proposes a novel AutoContext Model integrated with GANs and a gradient difference loss for improved MRI-to-CT image synthesis.

Findings

Outperforms three state-of-the-art methods

Produces more realistic CT images

Demonstrates robustness and accuracy

Abstract

Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiations. Therefore, recently, researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network to generate CT given an MR image. To better model the nonlinear relationship from MRI to CT and to produce more realistic images, we propose to use the adversarial training strategy and an image gradient difference loss function. We further apply AutoContext Model to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MRI images, and also outperforms three state-of-the-art methods under comparison.