Research Progress on Generative Adversarial Network in Cross-modal Medical Image Reconstruction

Abstract

Single-modal medical images contain limited disease-specific information. To analyze and diagnose patients, clinicians often need to integrate multiple modal images. However, due to limited medical resources and treatment time, it may be difficult to obtain multi-modal images. Cross-modal image reconstruction can generate medical images for clinical needs, thus assisting clinicians in accurately diagnosing and treating diseases. Traditional cross-modal reconstruction techniques have been applied in some clinical scenarios, but the quality of the reconstructed images needs further improvement. Generative adversarial network (GAN) can recover high-quality and complete image data from low-quality or incomplete medical image data, maximally savings medical equipment resources and accelerating medical treatment speed. This article summarizes the applications of GAN technology in cross-modal image reconstruction across X-ray imaging, computed tomography imaging, magnetic resonance imaging, and positron emission tomography imaging, to provide reference for the development of more advanced cross-modal reconstruction techniques.

Keywords

• cross-modal reconstruction
• radiological images
• generative adversarial network