Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
562-04-005 ISMRM Abstract

cSVR: Fast Convolutional Slice-to-Volume Reconstruction for Fetal Brain MRI

Primary: Pediatrics - Fetal
Secondary: Analysis Methods - Image Registration and Alignment
562-04-005 · AI/DL in Pediatrics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row C
Keywords: AI/ML Image Reconstruction Motion Correction Fetal MRI Deep learning image registration Slice-to-volume alignment
Accepted
Margherita Firenze 1, Sean I Young2, Clinton Wang1, Elfar Adalsteinsson3, Hyuk Jin Yun4,5, Patricia E Grant6, Kiho Im6, Polina Golland1
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, Cambridge, United States of America
2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States of America
4Department of Radiology, Children's Mercy Hospital, Kansas City, United States of America
5School of Medicine, University of Missouri-Kansas City, Kansas City, United States of America
6Boston Children's Hospital and Harvard Medical School, Boston, United States of America
Presenting Author: Margherita Firenze

Synopsis

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References

1. J. Xu, D. Moyer, B. Gagoski, J. E. Iglesias, P. E. Grant, P. Golland, and E. Adalsteinsson, “Nesvor: Implicit neural representation for slice-to-volume reconstruction in mri,” IEEE Transactions on Medical Imaging, vol. 42, no. 6, pp. 1707–1719, 2023. doi: 10.1109/TMI.2023.3236216 [doi]
2. Kuklisova-Murgasova, G. Quaghebeur, M. A. Rutherford, J. V. Hajnal, and J. A. Schnabel, “Reconstruction of fetal brain mri with intensity matching and complete outlier removal,” Medical Image Analysis, vol. 16, no. 8, pp. 1550–1564, 2012. [Online]. doi: 10.1016/j.media.2012.07.004 [doi]
3. A. Gholipour, J. A. Estroff, and S. K. Warfield, “Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI,” IEEE transactions on medical imaging, vol. 29, no. 10, pp. 1739– 1758, 2010. doi: 10.1109/TMI.2010.2051680 [doi]
4. S. I. Young, Y. Balbastre, B. Fischl, P. Golland, and J. E. Iglesias, “Fully convolutional slice-to-volume reconstruction for single-stack mri,” 2024. doi: 10.48550/arXiv.2312.03102 [doi]
5. Xu, D. Moyer, P. E. Grant, P. Golland, J. E. Iglesias, and E. Adalsteins- son, “Svort: Iterative transformer for slice-to-volume registration in fetal brain mri,” in Medical Image Computing and Computer Assisted Interven- tion – MICCAI 2022. doi: 10.48550/arXiv.2206.10802 [doi]
6. Payette, P. de Dumast, H. Kebiri, I. Ezhov, J. Paetzold, S. Shit, A. Iqbal, R. Khan, R. Kottke, P. Grehten, H. Ji, L. Lanczi, M. Nagy, B. Monika, T. Nguyen, G. Natalucci, T. Karayannis, B. Menze, M. Bach Cuadra, and A. Jakab, “An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset,” Scientific Data, vol. 8, 07 2021. doi: 10.1038/s41597-021-00946-3 [doi]
7. K. Payette, P. de Dumast, H. Kebiri, I. Ezhov, J. Paetzold, S. Shit, A. Iqbal, R. Khan, R. Kottke, P. Grehten, H. Ji, L. Lanczi, M. Nagy, B. Monika, T. Nguyen, G. Natalucci, T. Karayannis, B. Menze, M. Bach Cuadra, and A. Jakab, “An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset,” Scientific Data, vol. 8, 07 2021. doi: 10.1038/s41598-017-00525-w [doi]
8. M. B. M. Ranzini, L. Fidon, S. Ourselin, M. Modat, and T. Vercauteren, “Monaifbs: Monai-based fetal brain mri deep learning segmentation,” 2021. doi: 10.48550/arXiv.2103.13314 [doi]

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http://echo.ismrm.org/p/ISMRM2026/562-04-005