Abstract
Fetal cerebral brain magnetic resonance imaging (MRI) is critical for the detection of abnormal brain development before birth. A key image processing step is the reconstruction of a 3D high resolution volume from the acquired series of 2D slices. Several types of MR sequences are commonly acquired during a scanning session, but current reconstruction methods consider each sequence (or contrast) separately. Multi-contrast techniques have been proposed but they do not compensate for potential movement during the acquisition, which occurs almost systematically in the context of fetal MRI. In this work, we introduce a new method for the joint reconstruction of multiple 3D volumes from different contrasts. Our method combines the redundant and complementary information across several stacks of 2D slices from different acquisition sequences via an implicit neural representation, and includes a slice motion correction module. Our results on both simulations and real data acquired in clinical routine demonstrates the relevance and efficiency of the proposed method.
G. Auzias and F. Rousseau—These authors contributed equally to this work.
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
Cordero-Grande, L., et al.: Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration. IEEE Trans. Med. Imaging 42(3), 810–822 (2023). https://doi.org/10.1109/TMI.2022.3217725
Ebner, M., et al.: An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. Neuroimage 206, 116324 (2020). https://doi.org/10.1016/j.neuroimage.2019.116324
Edwards, A.D., et al.: The developing human connectome project neonatal data release. Front. Neurosci. 16, 886772 (2022). https://doi.org/10.3389/fnins.2022.886772
Feng, C.M., et al.: Exploring separable attention for multi-contrast MR image super-resolution. arXiv:2109.01664 (2022)
Girard, N., et al.: MR imaging of acquired fetal brain disorders. Childs Nerv. Syst. 19(7–8), 490–500 (2003). https://doi.org/10.1007/s00381-003-0761-x
Huang, J., et al.: Fast multi-contrast MRI reconstruction. Magn. Reson. Imaging 32(10), 1344–1352 (2014). https://doi.org/10.1016/j.mri.2014.08.025
Kuklisova-Murgasova, M., et al.: Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med. Image Anal. 16(8), 1550–1564 (2012). https://doi.org/10.1016/j.media.2012.07.004
Lei, P., et al.: Decomposition-based variational network for multi-contrast MRI super-resolution and reconstruction. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 21239–21249. IEEE, Paris, France (2023). https://doi.org/10.1109/ICCV51070.2023.01947
Lyu, Q., et al.: Multi-contrast super-resolution MRI through a progressive network. IEEE Trans. Med. Imaging 39(9), 2738–2749 (2020). https://doi.org/10.1109/TMI.2020.2974858
Manjón, J.V., et al.: MRI superresolution using self-similarity and image priors. Int. J. Biomed. Imaging 2010, 425891 (2010). https://doi.org/10.1155/2010/425891
McGinnis, J., et al.: Single-subject multi-contrast MRI super-resolution via implicit neural representations. arXiv:2303.15065 (2023)
Müller, T., et al.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41(4), 1–15 (2022). https://doi.org/10.1145/3528223.3530127
Rousseau, F., et al.: Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad. Radiol. 13(9), 1072–1081 (2006). https://doi.org/10.1016/j.acra.2006.05.003
Rousseau, F., et al.: A non-local approach for image super-resolution using intermodality priors. Med. Image Anal. 14(4), 594–605 (2010). https://doi.org/10.1016/j.media.2010.04.005
Sitzmann, V., et al.: Implicit neural representations with periodic activation functions. arXiv:2006.09661 (2020)
Uus, A., et al.: Multi-channel spatio-temporal MRI atlas of the normal fetal brain development from the developing human connectome project. IEEE Trans. Med. Imaging (6) (2023). https://doi.org/10.12751/g-node.ysgsy1
Xu, J., et al.: NeSVoR: implicit neural representation for slice-to-volume reconstruction in MRI. IEEE Trans. Med. Imaging 42(6), 1707–1719 (2023). https://doi.org/10.1109/tmi.2023.3236216
Zeng, K., et al.: Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network. Comput. Biol. Med. 99, 133–141 (2018). https://doi.org/10.1016/j.compbiomed.2018.06.010
Acknowledgments
The research leading to these results has also been supported by the ANR AI4CHILD Project, Grant ANR-19-CHIA-0015, the ANR SulcalGRIDS Project, Grant ANR-19CE45-0014, the ERA-NET NEURON MULTI-FACT Project, Grant ANR-21-NEU2-0005 and the ANR HINT Project, Grant ANR-22-CE45-0034 funded by the French National Research Agency. Data were provided by the developing Human Connectome Project, KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial.
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
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jia, S., Mercier, C., Pron, A., Girard, N., Auzias, G., Rousseau, F. (2025). Joint Multi-contrast Reconstruction of Fetal MRI Based on Implicit Neural Representations. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2024. Lecture Notes in Computer Science, vol 14747. Springer, Cham. https://doi.org/10.1007/978-3-031-73260-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-73260-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-73259-1
Online ISBN: 978-3-031-73260-7
eBook Packages: Computer ScienceComputer Science (R0)