Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-01-001 ISMRM Abstract

Rapid High-Fidelity Abdominal sCT Generation for MR-Only Radiotherapy on an MR-Linac with a 2.5D Hybrid VAE-GAN

Primary: Analysis Methods - Multi-Modal Learning with LLMs/VLMs
Secondary: Analysis Methods - Generative Models
561-01-001 · Generative Models for Image Synthesis · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row B
Keywords: Magnetic Resonance Imaging (MRI) Deep learning Synthetic CT Computed Tomography Variational Autoencoder
Accepted
Sukhraj Virdee1,2, Anna Briskina3, Amir Moslemi4, Angus Lau2,5,6,7,8, Brige P Chugh 2,5,6,7,9,10,11,12
1Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
2Medical Physics, Odette Cancer Centre, Toronto, Canada
3Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada
4medical physics, Grand River Regional Cancer Centre, Kitchener, Canada
5Department of Medical Biophysics, University of Toronto, Toronto, Canada
6Physical Science Platform, Sunnybrook Research Institute, Toronto, Canada
7Department of Radiation Oncology, University of Toronto, Toronto, Canada
8Medical Biophysics, University of Toronto, Toronto, Canada
9University of Toronto, Toronto, Canada
10Sunnybrook Research Institute, Toronto, Canada
11Toronto Metropolitan University, Toronto, Canada
12Department of Physics, Toronto Metropolitan University, Toronto, Canada
Presenting Author: Brige P Chugh

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References

1. Hall WA, Paulson ES, van der Heide UA, et al. The transformation of radiation oncology using real-time magnetic resonance guidance: a review. Eur J Cancer. 2019;122:42-52. doi:10.1016/j.ejca.2019.07.021 [doi]
2. Owrangi AM, Greer PB, Glide-Hurst CK. MRI-only treatment planning: benefits and challenges. Phys Med Biol. 2018;63(5):05TR01. doi:10.1088/1361-6560/aaaca4 [doi]
3. Johnstone E, Wyatt JJ, Henry AM, et al. Systematic review of synthetic computed tomography generation methodologies for use in magnetic resonance imaging-only radiation therapy. Int J Radiat Oncol Biol Phys. 2018;100(1):199-217. doi:10.1016/j.ijrobp.2017.08.043 [doi]
4. Keall PJ, Mageras GS, Balter JM, et al. The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys. 2006;33(10):3874-3900. doi:10.1118/1.2349696 [doi]
5. Rehman A, Khan FG. A deep learning based review on abdominal images. Multimed Tools Appl. 2021;80(20):30321-30352. doi:10.1007/s11042-020-09592- [doi]
6. Avants BB, Tustison NJ, Song G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54(3):2033-2044. doi:10.1016/j.neuroimage.2010.09.025 [doi]
7. Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. Proc IEEE Conf Comput Vis Pattern Recognit. 2017:1125-1134. doi:10.1109/CVPR.2017.632 [doi]
8. Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution. arXiv. Published online March 27, 2016. arXiv:1603.08155.
9. Kullback S, Leibler RA. On information and sufficiency. Ann Math Stat. 1951;22(1):79-86. doi:10.1214/aoms/1177729694 [doi]
10. Loshchilov I, Hutter F. Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations (ICLR). 2019.

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http://echo.ismrm.org/p/ISMRM2026/561-01-001