Research demonstrates effective super-resolution in fetal brain MRI images, suggesting new approaches to resolve motion artifacts.
Motivation: Super-resolution reconstruction of isotropic fetal brain MR images is critical for prenatal examinations, but is hindered by fetal motion and misalignment of thick-slice scans. Goal(s): This study aims to quantify misalignment between slices and volumes while predicting isotropic high-resolution volumetric images. Approach: Our approach learns an implicit function to quantify misalignment between MR slices and volumes by obtaining interpolation weights from latent codes of multi-view motion-corrupted thick-slice stacks. The established model then interpolates the isotropic high-resolution image by utilizing predicted weights. Results: Our proposed method effectively mitigates the adverse effects of motion corruption in fetal brain MRI while substantially reducing reconstruction time. Impact: Our end-to-end fetal brain super-resolution approach bypasses traditional two-step iterative optimization paradigm, and substantially reduces reconstruction time. It eliminates the need for slice-to-volume registration, and shifts the focus of implicit neural representation from addressing appearance estimation issues to motion estimation.
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Huang et al. (2025) studied this question.