In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or improve Signal to Noise Ratio (SNR). In those cases, a previously acquired image can serve as a reference image, that may exhibit similarity in some sense to the image being acquired. Examples include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scans and similarity between different scans of the same patients. In this paper we present a general framework for utilizing reference images for fast MRI. We take into account that the reference image may exhibit low similarity with the acquired image and develop a hybrid adaptive-weighted approach for sampling and reconstruction. Experiments demonstrate the performance of the method in three different clinical MRI scenarios: SNR improvement in high resolution brain MRI, utilizing similarity between T2-weighted and fluid-attenuated inversion recovery (FLAIR) for fast FLAIR scanning and utilizing similarity between baseline and follow-up scans for fast follow-up scanning.