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Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modalityfor spine pathologies with excellent characterization for infection, tumor,degenerations, fractures and herniations. However in surgery, image-guidedspinal procedures continue to rely on CT and fluoroscopy, as MRI sliceresolutions are typically insufficient. Building upon state-of-the-art singleimage super-resolution, we propose a reference-based, unpaired multi-contrasttexture-transfer strategy for deep learning based in-plane and across-plane MRIsuper-resolution. We use the scattering transform to relate the texturefeatures of image patches to unpaired reference image patches, and additionallya loss term for multi-contrast texture. We apply our scheme in differentsuper-resolution architectures, observing improvement in PSNR and SSIM for 4xsuper-resolution in most of the cases.

Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images