Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
561-01-002
ISMRM Abstract
Automated FLAIR Synthesis from T1 and T2 Brain MRI at 3T
Primary:
Analysis Methods - Image Synthesis and Translation
Secondary:
Analysis Methods - Generative Models
561-01-002 · Generative Models for Image Synthesis
· Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row B
Keywords:Analysis/ProcessingBrainSynthetic MRI
Accepted
Yaozhong Huang1, Patricia M Johnson1,2, Narges Razavian1,3, Riccardo Lattanzi 1,2
1Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States of America
2Center for Advanced Imaging Innovation and Research (CAI²R), New York University Grossman School of Medicine, New York, United States of America
3Department of Population Health, New York University Grossman School of Medicine, New York, United States of America
Presenting Author: Riccardo Lattanzi
Synopsis
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1. Bakshi R, Ariyaratana S, Benedict RHB, Jacobs L. Fluid-Attenuated Inversion Recovery Magnetic Resonance Imaging Detects Cortical and Juxtacortical Multiple Sclerosis Lesions. Archives of Neurology. 2001;58(5):742. doi:https://doi.org/10.1001/archneur.58.5.742 [doi]
2. Mikheev A, Nevsky G, Govindan S, Grossman R, Rusinek H. Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm. Journal of Magnetic Resonance Imaging. 2008;27(6):1235-1241. doi:https://doi.org/10.1002/jmri.21372 [doi]
4. He K, Chen X, Xie S, Li Y, Dollar P, Girshick R. Masked Autoencoders Are Scalable Vision Learners. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2022. doi:https://doi.org/10.1109/cvpr52688.2022.01553 [doi]
5. Hatamizadeh A, Nath V, Tang Y, Yang D, Roth H, Xu D. Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv:220101266 [cs, eess]. Published online January 4, 2022. https://arxiv.org/abs/2201.01266
6. Zhang L, Rao A, Maneesh Agrawala. Adding Conditional Control to Text-to-Image Diffusion Models. 2023 IEEE/CVF International Conference on Computer Vision (ICCV). Published online October 1, 2023. doi:https://doi.org/10.1109/iccv51070.2023.00355 [doi]
7. Wang Z, Simoncelli EP, Bovik AC. Multiscale structural similarity for image quality assessment. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. Published online 2019. doi:https://doi.org/10.1109/acssc.2003.1292216 [doi]
8. Pinto C, Noronha C, Taipa R, Ramos C. T2-FLAIR mismatch sign: a roadmap of pearls and pitfalls. The British Journal of Radiology. 2022;95(1129). doi:https://doi.org/10.1259/bjr.20210825 [doi]
9. Al-Fakih A, Shazly A, Mohammed A, et al. FLAIR MRI sequence synthesis using squeeze attention generative model for reliable brain tumor segmentation. Alexandria Engineering Journal. 2024;99:108-123. doi:https://doi.org/10.1016/j.aej.2024.05.008 [doi]
10. Dalmaz O, Yurt M, Çukur T. ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis. IEEE Transactions on Medical Imaging. 2022;41(10):2598-2614. doi:https://doi.org/10.1109/TMI.2022.3167808 [doi]