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- W3126363532 abstract "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." @default.
- W3126363532 created "2021-02-15" @default.
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- W3126363532 date "2021-02-10" @default.
- W3126363532 modified "2023-09-26" @default.
- W3126363532 title "Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images" @default.
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