Multi-contrast isotropic high resolution intracranial vessel wall imaging (VWI) can enable direct detection and follow-up surveillance of intracranial vessel wall pathologies. The overall scan time can be largely reduced by proper random undersampling. However, the image reconstruction process can be time consuming causing deployment difficulties of accelerated intracranial VWI for clinical use. In this study, a generative adversarial networks based compressed sensing method was developed for multi-contrast intracranial image reconstruction. The preliminary results demonstrate comparable/improved image quality for vessel wall delineation in comparison to the traditional image reconstruction method, while providing a significant reduction in reconstruction time.