Abstract
Super-resolution reconstruction (SRR) of fetal brain MRI from motion-corrupted thick-slice stacks can provide high-resolution isotropic 3D images that are vital for prenatal examination and quantification of brain development. Existing fetal brain SRR methods generally rely on a two-stage optimization procedure by performing rigid slice-to-volume registration and volumetric reconstruction in an alternating manner. Despite their advantages, these methods have not considered additional guidance from external anatomical priors, resulting in unsatisfactory performance in various challenging cases. To address this issue, we propose a novel Prior Anatomical Knowledge guided fetal brain Super-Resolution Reconstruction method, namely PAK-SRR. In PAK-SRR, we consider two key kinds of prior anatomical information. First, we integrate the anatomical prior provided by tissue segmentation into both the slice-to-volume registration and volumetric reconstruction to enforce registration consistency on boundaries, effectively alleviating misregistration caused by blurry tissue boundaries of brain. Second, to enrich the structural details of the reconstructed images, we further employ longitudinal fetal brain atlases to guide volumetric reconstruction. Extensive experiments on multi-site clinical datasets demonstrate that our PAK-SRR significantly outperforms the state-of-the-art SRR methods for fetal brain MRI, quantitatively and qualitatively. Our code is publicly available at https://github.com/sj-huang/PAK-SRR for reproducibility and further research.
S. Huang and G. Chen— Contributed equally.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China (grant number 62131015), Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R &D Program of Guangdong Province, China (grant number 2021B0101420006).
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Huang, S. et al. (2023). Super-Resolution Reconstruction of Fetal Brain MRI with Prior Anatomical Knowledge. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham. https://doi.org/10.1007/978-3-031-34048-2_33
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