A self-supervised super-resolution reconstruction method based on scale adaptive and coordinate encoding is proposed to realize super-resolution reconstruction of anisotropic brain magnetic resonance images with different slice thicknesses even in the absence of paired isotropic brain magnetic resonance images.Firstly,an image encoding module that integrates super-resolution scale information is used to learn the specific features of images with different slice thicknesses.Subsequently,a coordinate encoding module is employed to facilitate the deep fusion of coordinate information and image features.Finally,an overall loss function comprising reconstruction loss and brain tissue edge perception loss is adopted to optimize the recovery of edge high-frequency information,while global residual learning is introduced to enhance network training.Experimental results on the HCP-1200 and OASIS-1 datasets demonstrate that the proposed method outperforms other self-supervised super-resolution reconstruction methods.

A self-supervised super-resolution reconstruction method based on scale adaptive and coordinate encoding is proposed to realize super-resolution reconstruction of anisotropic brain magnetic resonance images with different slice thicknesses even in the absence of paired isotropic brain magnetic resonance images.Firstly,an image encoding module that integrates super-resolution scale information is used to learn the specific features of images with different slice thicknesses.Subsequently,a coordinate encoding module is employed to facilitate the deep fusion of coordinate information and image features.Finally,an overall loss function comprising reconstruction loss and brain tissue edge perception loss is adopted to optimize the recovery of edge high-frequency information,while global residual learning is introduced to enhance network training.Experimental results on the HCP-1200 and OASIS-1 datasets demonstrate that the proposed method outperforms other self-supervised super-resolution reconstruction methods.