Super-Resolution Reconstruction of Fetal Brain MRI With Multi-View Interpolation Weight Learning

Super-resolution reconstruction (SRR) of isotropic fetal brain MR images is critical for prenatal examinations but is hindered by fetal motion and misalignment of thick-slice scans. To address these challenges comprehensively, we introduce an innovative deep learning model, namely 3D-WISE, a 3D Weighted Interpolation for Super-resolution Estimation of fetal brain MRI. The model generates high-quality isotropic fetal brain MR images by learning the interpolation weights to correct misalignments between slices and volumes. These misalignments are estimated by extracting deep features from multiple motion-corrupted stacks. Specifically, 3D-WISE incorporates two key components: (1) a weight learning module for multi-view interpolation and (2) a feature extraction module guided by multi-type attention mechanisms. The weight learning module first maps motion-corrupted thick-slice stacks into latent feature spaces. The resulting features are then fed to an implicit decoding block to estimate interpolation weights of the surrounding points for a given coordinate. We further enhance our approach by incorporating convolutional block attention and atlas-induced cross-attention mechanisms. Extensive experiments on two benchmark datasets show that our 3D-WISE achieves remarkably improved performance compared to the widely adopted registration-reconstruction framework. We also extend the experiments on anatomical structure reconstruction and achieve promising results, highlighting the significant potential of our 3D-WISE for fetal brain MR images SRR in clinical settings.