Abstract
Fetal brain magnetic resonance imaging (MRI) is becoming more important for early brain assessment in prenatal examination. Fast acquisition of three cross-sectional series/views is often used to eliminate motion effects using single-shot fast spin-echo sequences. Although stacked in 3D volumes, these slices are essentially 2D images with large slice thickness and distances (4 to 6 mm) resulting blurry multiplanar views. To better visualize and quantify fetal brains, it is desirable to reconstruct 3D images from different 2D cross-sectional series. In this paper, we present a super-resolution CNN-based network for 3D image reconstruction using unsupervised learning, referred to as cross-sectional image reconstruction (C-SIR). The key idea is that different cross-sectional images can help each other for training the C-SIR model. Additionally, existing high resolution data can also be used for pre-training the network in a supervised manner. In experiments, we show that such a network can be trained to reconstruct 3D images using simulated down-sampled adult images with much better image quality and image segmentation accuracy. Then, we illustrate that the proposed C-SIR approach generates relatively clear 3D fetal images than other algorithms.
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References
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)
Ebner, M., et al.: An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. Neuroimage 206, 116324 (2020)
Kainz, B., et al.: Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans. Med. Imaging 34(9), 1901–1913 (2015)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)
Meshaka, R., Gaunt, T., Shelmerdine, S.C.: Artificial intelligence applied to fetal MRI: a scoping review of current research. Br. J. Radiol. 96(1147), 20211205 (2023)
Nagaraj, U.D., Kline-Fath, B.M.: Clinical applications of fetal MRI in the brain. Diagnostics 12(3), 764 (2022)
Saleem, S.: Fetal MRI: an approach to practice: a review. J. Adv. Res. 5(5), 507–523 (2014)
Sui, Y., Afacan, O., Jaimes, C., Gholipour, A., Warfield, S.K.: Scan-specific generative neural network for MRI super-resolution reconstruction. IEEE Trans. Med. Imaging 41(6), 1383–1399 (2022)
Wu, S., Sun, Y., Zhou, X., Gong, X., Yang, Y.: Magnetic resonance imaging: principles and recent advances. J. Med. Syst. 42(8), 148 (2018)
Zhang, J., Cui, Z., Jiang, C., Guo, S., Gao, F., Shen, D.: Hierarchical organ-aware total-body standard-dose PET reconstruction from low-dose PET and CT images. IEEE Transactions on Neural Networks and Learning Systems (2023)
Zhang, J., Cui, Z., Jiang, C., Zhang, J., Gao, F., Shen, D.: Mapping in cycles: dual-domain PET-CT synthesis framework with cycle-consistent constraints. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol. 13436, pp. 758–767. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16446-0_72
Acknowledgement
This work was partially supported by The Key R and D Program of Guangdong Province, China (grant number 2021B0101420006), National Natural Science Foundation of China (grant number 62131015), Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600) and the National Key Research and Development Program of China (2022ZD0209000).
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Yang, Y. et al. (2024). Reconstruction of 3D Fetal Brain MRI from 2D Cross-Sectional Acquisitions Using Unsupervised Learning Network. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham. https://doi.org/10.1007/978-3-031-45673-2_4
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DOI: https://doi.org/10.1007/978-3-031-45673-2_4
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