Reference guided CS-MRI with gradient orientation priors

The theory of Compressed sensing (CS) provides a systematic framework for MR image reconstruction from under-sampled k-space data. However, severe aliasing artifacts still occurs in the case of high acceleration. Thereupon, an extensive body of works investigates exploiting additional prior information extracted from a reference image which can be acquired with relative ease in many MR applications. In this work, we propose a novel reference guided CS-MRI reconstruction method using gradient orientation priors (GOP). Specifically, we regularize the tangent vector in the target image to be perpendicular to the corresponding normal vector in the reference image over all spatial locations. The proposed method was validated using multi-scan experiment data and is shown to provide high speed and high quality imaging.