The theory of compressed sensing (CS) provides a systematic framework for magnetic resonance (MR) image reconstruction from incoherently under-sampled k-space data. However, severe aliasing artifacts may still occur in cases of high acceleration and noisy measurements. Thereupon, an extensive body of work investigates exploiting additional prior information extracted from a reference image which can be acquired with relative ease in many MR applications. In this work, a CS-based MR image reconstruction method using reference gradient orientation priors was proposed. Specifically, the tangent vector in the target image was regularized to be perpendicular to the corresponding normal vector in the reference image over all spatial locations to make the gradient orientations in the reference and the target image consistent. The proposed method is validated using multi-scan experiment data and is shown to provide high speed and high quality imaging.