Super-resolution Reconstruction of Fetal Brain MRI with Multi-view Interpolation Weight Learning.
Authors
Abstract
Super-resolution reconstruction (SRR) of isotropic fetal brain MR images is critical for prenatal ex aminations 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 adoptedregistration-reconstruction framework. We also ex tend 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. Our code is available at https: //github.com/sj-huang/3D-WISE.