In MRI, region-of-interest (ROI) imaging is frequently used in clinical applications. The sub-sampling-based scheme is capable of accelerating the ROI-focused image reconstruction process but degrades the image quality. The degradation could be alleviated by ROI-weighted optimization; however, existing methods mainly focus on the local signal restoration and have no explicit control of the noise from the entire image. In this abstract, we propose to reconstruct the ROI using a non-local U-net method that incorporates contextual information from the whole image. The results show the proposed algorithm improves PSNR and SSIM over conventional methods.