Spatially Aware Interpolation Synthesis Method for MR Images Based on Asymmetric Mask Coding

Because of the inherent constraints in the hardware capabilities of magnetic resonance imaging (MRI), the process of image acquisition is comparatively sluggish, and the resolution among slices is subpar, leaving clinicians with incomplete structural details about specific bodily regions. Current approaches to super-resolution interpolation for medical slices predominantly cater to individual slices, neglecting the valuable correlational information that exists between slices. This oversight substantially hampers the efficacy of interpolation synthesis. Furthermore, mainstream super-resolution techniques are tailored to specific up-sampling scenarios, complicating their application in clinical settings. To overcome these hurdles, this paper introduces an MRI spatial-aware interpolation synthesis method utilizing asymmetric mask encoding. This method employs an asymmetric network model to concurrently achieve high-resolution interpolation synthesis across multiple interpolation factors. The proposed MRI spatial-aware interpolation network, powered by asymmetric mask encoding, effectively harnesses correlational insights from diverse MRI image perspectives. Validation of this approach was conducted using a subset of data from the MICCAI (Medical Image Computing and Computer-Assisted Intervention) BraTS (Brain Tumor Segmentation) 2018 challenge T1 dataset, yielding high-resolution images with multiple interpolation factors.