Research on the Modality Transfer Method of Brain Imaging Based on Generative Adversarial Network

Abstract

Brain imaging technology is an important means to study brain diseases. The commonly used brain imaging technologies are fMRI and EEG. Clinical practice has shown that although fMRI is superior to EEG in observing the anatomical details of some diseases that are difficult to diagnose, its costs are prohibitive. In particular, more and more patients who use metal implants cannot use this technology. In contrast, EEG technology is easier to implement. Therefore, to break through the limitations of fMRI technology, we propose a brain imaging modality transfer framework, namely BMT-GAN, based on a generative adversarial network. The framework introduces a new non-adversarial loss to reduce the perception and style difference between input and output images. It also realizes the conversion from EEG modality data to fMRI modality data and provides comprehensive reference information of EEG and fMRI for radiologists. Finally, a qualitative and quantitative comparison with the existing GAN-based brain imaging modality transfer approaches demonstrates the superiority of our framework.

Keywords: EEG; brain imaging modality transfer; fMRI; generative adversarial network; non-adversarial loss.