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Abstract #0441

MR image super-resolution using attention mechanism: transfer textures from external database

Mengye Lyu1, Guoxiong Deng1, Yali Zheng1, Yilong Liu2,3, and Ed X. Wu2,3
1College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, 2Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 3Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

Super-resolution (SR) is useful to reduce scan time and/or enhance MR images for better visual perception. High-resolution reference images may improve super-resolution quality, but most previous studies focused on using references from the same subject. Here, we use an image search module to find similar images from other subjects and use transformer based neural networks to learn and transfer the relevant textures to the output. We demonstrate that this approach can outperform single-image super-resolution, and is feasible to achieve high-quality super-resolution at large factors. As the reference images are not limited within a subject, it potentially has wide applications.

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