An Integrated Automatic Framework for Super-Resolution Reconstruction of Motion-Corrupted Fetal Brain MRI with Prior Anatomical Knowledge

Super-resolution reconstruction (SRR) from motion-corrupted thick-slice stacks of fetal brain MR images is essential for comprehensive prenatal examination and precise quantification of fetal brain development. Conventional approaches for fetal brain SRR require human intervention to extract cerebral structures from 2D slices, and their performance is often limited by blurred and less informative acquisitions due to fast scanning or irregular fetal movement. In order to overcome these challenges, we propose an automatic fetal brain SRR framework by integrating both individual- and group-level priors of fetal brain MRI for improved SRR. Specifically, we develop a robust fetal brain extraction approach based on Segment Anything Model (SAM). The extracted fetal brain region is segmented into brain tissues to serve as anatomical prior for the follow-up SRR. The SRR is performed based on an iterative optimization scheme by alternatingly performing slice-to-volume registration and volumetric reconstruction. Specifically, we ingeniously integrate anatomical priors obtained by tissue segmentation into both slice-to-volume registration and volumetric reconstruction, which emphasizes boundary alignment during registration and mitigates misalignment stemming from indistinct boundaries of cerebral tissues. Furthermore, we harness the available longitudinal fetal brain atlases to serve as specific guidance for volumetric reconstruction, thereby enriching structural details of the reconstructed images and also circumventing reconstruction of outliers. Experimental results on 184 clinical fetal brain MR images show that our proposed framework largely outperforms state-of-the-art methods for fetal brain SRR quantitatively and qualitatively.