Magnetic resonance imaging (MRI) has high resolution for soft tissues and significant advantages in the examination of the bone and joint systems and central nervous system. However, due to cost and usage limitations, MRI cannot be applied in various scenarios. Computer tomography (CT), as a fast medical examination technique that does not require special preparation, has a wider range of applications, but its accuracy in organ imaging is lower than MRI. At present, image conversion methods suffer from low resolution and low signal-to-noise ratio in synthesizing MR images. In addition, these methods have limited capabilities and cannot accurately synthesize MR images with diverse anatomical and functional differences. Therefore, this article proposes a multimodal medical image conversion method based on Generative Adversarial Networks (GAN) to convert CT images into MR images. This method uses two parallel generative adversarial networks to generate two MR images to compensate for the missing generation of a single generative adversarial network. The experimental results demonstrate that this method is effective and can generate MR images close to the reference MR image.
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