Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion (2402.13776v1)
Abstract: Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the brain structures. However, longitudinal MRI acquisition always meets a serious data-missing problem due to participant dropout and failed scans, making longitudinal infant brain atlas construction and developmental trajectory delineation quite challenging. Thanks to the development of an AI-based generative model, neuroimage completion has become a powerful technique to retain as much available data as possible. However, current image completion methods usually suffer from inconsistency within each individual subject in the time dimension, compromising the overall quality. To solve this problem, our paper proposed a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution. We applied our proposed method to the Baby Connectome Project (BCP) dataset. The experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion. We further applied the generated infant brain images to two downstream tasks, brain tissue segmentation and developmental trajectory delineation, to declare its task-oriented potential in the neuroscience field.
- “Imaging structural and functional brain development in early childhood,” Nature Reviews Neuroscience, vol. 19, no. 3, pp. 123–137, 2018.
- “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502, 2020.
- “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684–10695.
- “A novel unified conditional score-based generative framework for multi-modal medical image completion,” arXiv preprint arXiv:2207.03430, 2022.
- “Mm-diffusion: Learning multi-modal diffusion models for joint audio and video generation,” arXiv preprint arXiv:2212.09478, 2022.
- “Cascaded diffusion models for high fidelity image generation.,” J. Mach. Learn. Res., vol. 23, no. 47, pp. 1–33, 2022.
- “The unc/umn baby connectome project (bcp): An overview of the study design and protocol development,” NeuroImage, vol. 185, pp. 891–905, 2019.
- “Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age,” Cerebral cortex, vol. 23, no. 11, pp. 2724–2733, 2013.
- “Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy,” Nature Communications, vol. 13, no. 1, pp. 6566, 2022.
- “Longitudinally guided super-resolution of neonatal brain magnetic resonance images,” IEEE transactions on cybernetics, vol. 49, no. 2, pp. 662–674, 2018.
- “Neonatal brain image segmentation in longitudinal mri studies,” Neuroimage, vol. 49, no. 1, pp. 391–400, 2010.