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.
Highlights
Brain imaging modality transfer has become popular in the field of medical imaging, providing a variety of reference information for early diagnosis, identification, treatment, and follow-up of the diseases (Yi et al, 2019)
The contributions of this article are summarized as follows: (1) We proposed a new framework named BMT-generative adversarial network (GAN) to estimate two-dimensional fMRI images using two-dimensional EEG images; (2) BMT-GAN framework combines the cycle-consistent loss, adversarial loss, and non-adversarial loss to achieve excellent brain imaging modality transfer performance; (3) The proposed approach can be extended to other medical data translation tasks to benefit the medical imaging field
This paper proposes a novel brain imaging modality transfer framework, namely BMT-GAN
Summary
Brain imaging modality transfer has become popular in the field of medical imaging, providing a variety of reference information for early diagnosis, identification, treatment, and follow-up of the diseases (Yi et al, 2019). The information provided by an image obtained from a certain imaging method is often limited, and can only reflect the information of one modality, which generally cannot help the doctor to make an accurate diagnosis. The modality transfer technology is conducive to converting between different modality images to obtain multi-modality information. It can provide a variety of information about diseased tissues or organs, and provide a strong theoretical basis for clinical medicine to make an accurate diagnosis. As for the modality transfer of brain imaging, the current research at home and abroad is mainly based on the image-to-image translation. Existing image-to-image translation methods are mainly divided into two categories: sparse representation-based method and learning-based method
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