Medical Image Synthesis with Context-Aware Generative Adversarial Networks (1612.05362v1)

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.

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

The paper "Medical Image Synthesis with Context-Aware Generative Adversarial Networks" addresses the significant challenge of estimating CT images from corresponding MRI scans to mitigate the radiation exposure associated with CT imaging. The methodology proposed introduces a novel application of Generative Adversarial Networks (GANs) to medical image synthesis, specifically focusing on MRI to CT conversion.

Methodology Overview

The authors employ a 3D Fully Convolutional Network (FCN) as a generator within the GAN framework. This FCN is specifically designed to handle the 3D nature of medical images, preserving spatial information and minimizing discontinuities between image slices. The adversarial approach includes a discriminator network that differentiates real CT images from generated ones, pushing the generator to enhance the realism of the synthetic images.

To further address the limitations inherent in patch-based approaches, the authors integrate an Auto-Context Model (ACM), an iterative framework that consecutively refines the outputs, leveraging progressively more extensive contextual information. This setup enhances the ability of the GAN to produce high-fidelity CT images from MRI inputs.

Experimental Results

The paper presents a robust set of experimental validations across two datasets: one involving brain scans, and another with pelvic scans. The results demonstrate that the proposed method surpasses traditional and contemporary methods, such as atlas-based methods, sparse representation, and structured random forests integrated with Auto-Context.

Notably, the incorporation of image gradient difference loss, in addition to the conventional reconstruction error in the generator's loss function, significantly reduces artifacts and improves image quality, as evidenced by qualitative and quantitative measures such as Peak Signal-to-Noise Ratio (PSNR) and Mean Absolute Error (MAE).

Implications and Future Directions

This research substantiates the feasibility and advantages of employing GAN-based architectures for synthetic medical image generation. The approach mitigates radiation risks associated with CT scans in clinical settings, potentially extending to other imaging modalities and applications such as super-resolution and image denoising.

The paper serves as a precursor to future research directions, which may explore further architectural optimizations, the integration of multimodal inputs, or the extension of this framework to larger datasets and varied anatomical regions. Additionally, the method's applicability to real-time processing environments remains a promising avenue for exploration, given the computational intensity of GAN training.

Conclusion

This paper significantly contributes to the domain of medical imaging by demonstrating that GANs, when combined with context-aware methodologies like ACM, provide a potent tool for addressing the CT estimation problem from MRI data. The proposed framework achieves notable improvements over state-of-the-art methods and opens up several pathways for future inquiry and application in AI-driven medical image analysis.