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