Abstract
The infant brain undergoes rapid development in the first few years after birth. Compared to cross-sectional studies, longitudinal studies can depict the trajectories of infants’ brain development with higher accuracy, statistical power and flexibility. However, the collection of infant longitudinal magnetic resonance (MR) data suffers a notorious dropout problem, resulting in incomplete datasets with missing time points. This limitation significantly impedes subsequent neuroscience and clinical modeling. Yet, existing deep generative models are facing difficulties in missing brain image completion, due to sparse data and the nonlinear, dramatic contrast/geometric variations in the developing brain. We propose LoCI-DiffCom, a novel Longitudinal Consistency-Informed Diffusion model for infant brain image Completion, which integrates the images from preceding and subsequent time points to guide a diffusion model for generating high-fidelity missing data. Our designed LoCI module can work on highly sparse sequences, relying solely on data from two temporal points. Despite wide separation and diversity between age time points, our approach can extract individualized developmental features while ensuring context-aware consistency. Our experiments on a large infant brain MR dataset demonstrate its effectiveness with consistent performance on missing infant brain MR completion even in big gap scenarios, aiding in better delineation of early developmental trajectories.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: Vivit: A video vision transformer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6836–6846 (2021)
Dorjsembe, Z., Odonchimed, S., Xiao, F.: Three-dimensional medical image synthesis with denoising diffusion probabilistic models. In: Medical Imaging with Deep Learning (2022)
Gilmore, J.H., Knickmeyer, R.C., Gao, W.: Imaging structural and functional brain development in early childhood. Nature Reviews Neuroscience 19(3), 123–137 (2018)
Guo, L., Tao, T., Cai, X., Zhu, Z., Huang, J., Zhu, L., Gu, Z., Tang, H., Zhou, R., Han, S., et al.: Cas-diffcom: Cascaded diffusion model for infant longitudinal super-resolution 3d medical image completion. arXiv preprint arXiv:2402.13776 (2024)
Hazlett, H.C., Gu, H., McKinstry, R.C., Shaw, D.W., Botteron, K.N., Dager, S.R., Styner, M., Vachet, C., Gerig, G., Paterson, S.J., et al.: Brain volume findings in 6-month-old infants at high familial risk for autism. American Journal of Psychiatry 169(6), 601–608 (2012)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33, 6840–6851 (2020)
Holland, D., Chang, L., Ernst, T.M., Curran, M., Buchthal, S.D., Alicata, D., Skranes, J., Johansen, H., Hernandez, A., Yamakawa, R., et al.: Structural growth trajectories and rates of change in the first 3 months of infant brain development. JAMA neurology 71(10), 1266–1274 (2014)
Howell, B.R., Styner, M.A., Gao, W., Yap, P.T., Wang, L., Baluyot, K., Yacoub, E., Chen, G., Potts, T., Salzwedel, A., et al.: The unc/umn baby connectome project (bcp): An overview of the study design and protocol development. NeuroImage 185, 891–905 (2019)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017)
Kim, B., Ye, J.C.: Diffusion deformable model for 4d temporal medical image generation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 539–548. Springer (2022)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Knickmeyer, R.C., Gouttard, S., Kang, C., Evans, D., Wilber, K., Smith, J.K., Hamer, R.M., Lin, W., Gerig, G., Gilmore, J.H.: A structural mri study of human brain development from birth to 2 years. Journal of neuroscience 28(47), 12176–12182 (2008)
Kraemer, H.C., Yesavage, J.A., Taylor, J.L., Kupfer, D.: How can we learn about developmental processes from cross-sectional studies, or can we? American Journal of Psychiatry 157(2), 163–171 (2000)
Li, G., Nie, J., Wang, L., Shi, F., Lin, W., Gilmore, J.H., Shen, D.: Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. Cerebral cortex 23(11), 2724–2733 (2013)
Liu, Y., Shao, Z., Hoffmann, N.: Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv preprint arXiv:2112.05561 (2021)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)
Paterson, S.J., Heim, S., Friedman, J.T., Choudhury, N., Benasich, A.A.: Development of structure and function in the infant brain: Implications for cognition, language and social behaviour. Neuroscience & Biobehavioral Reviews 30(8), 1087–1105 (2006)
Peng, W., Adeli, E., Zhao, Q., Pohl, K.M.: Generating realistic 3d brain mris using a conditional diffusion probabilistic model. arXiv preprint arXiv:2212.08034 (2022)
Pinaya, W.H., Tudosiu, P.D., Dafflon, J., Da Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., Cardoso, M.J.: Brain imaging generation with latent diffusion models. In: MICCAI Workshop on Deep Generative Models. pp. 117–126. Springer (2022)
Shi, F., Fan, Y., Tang, S., Gilmore, J.H., Lin, W., Shen, D.: Neonatal brain image segmentation in longitudinal mri studies. Neuroimage 49(1), 391–400 (2010)
Shi, F., Hu, W., Wu, J., Han, M., Wang, J., Zhang, W., Zhou, Q., Zhou, J., Wei, Y., Shao, Y., et al.: Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy. Nature Communications 13(1), 6566 (2022)
Soh, S.E., Tint, M.T., Gluckman, P.D., Godfrey, K.M., Rifkin-Graboi, A., Chan, Y.H., Stünkel, W., Holbrook, J.D., Kwek, K., Chong, Y.S., et al.: Cohort profile: Growing up in singapore towards healthy outcomes (gusto) birth cohort study. International journal of epidemiology 43(5), 1401–1409 (2014)
Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)
Yoon, J.S., Zhang, C., Suk, H.I., Guo, J., Li, X.: Sadm: Sequence-aware diffusion model for longitudinal medical image generation. In: International Conference on Information Processing in Medical Imaging. pp. 388–400. Springer (2023)
Zhang, C., Adeli, E., Wu, Z., Li, G., Lin, W., Shen, D.: Infant brain development prediction with latent partial multi-view representation learning. IEEE transactions on medical imaging 38(4), 909–918 (2018)
Zhang, Y., Shi, F., Cheng, J., Wang, L., Yap, P.T., Shen, D.: Longitudinally guided super-resolution of neonatal brain magnetic resonance images. IEEE transactions on cybernetics 49(2), 662–674 (2018)
Acknowledgments
This work is supported by the STI 2030-Major Projects (No. 2022 ZD0209000), National Natural Science Foundation of China (No. 62203355), Shanghai Pilot Program for Basic Research - Chinese Academy of Science, Shanghai Branch (No. JCYJ-SHFY-2022-014), Shenzhen Science and Technology Program (No. KCXFZ 20211020163408012), and Shanghai Pujiang Program (No. 21PJ1421400).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, Z. et al. (2024). LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15002. Springer, Cham. https://doi.org/10.1007/978-3-031-72069-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-72069-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72068-0
Online ISBN: 978-3-031-72069-7
eBook Packages: Computer ScienceComputer Science (R0)
