Significance of magnetic resonance image details in sparse representation based super resolution

Abstract

Diverse constraints on image acquisition environment often limit the resolution in cross-slice direction of Magnetic Resonance (MR) image volume, which does not meet the requirement of isotropic 3D MR images in accurate medical diagnosis. This paper proposes an algorithm to restore isotropic 3D MR images from anisotropic 2D multi-slice volumes, by preserving the MR details that play significant role in medical diagnosis. The MR image details are preserved using dictionaries, which are learned using fine to coarse patch details, extracted from different scales of MR image. Learned dictionaries provide detail information for restoring MR patch details. Furthermore, a constraint is used to preserve edges within the restored MR image by minimizing an energy cost. Here, the constraint is weighted adaptively according to the dominant edge orientation of the image, to preserve the details along different orientations effectively. Experimental results demonstrate the ability of our approach to preserve MR image details.