Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-01-006 ISMRM Abstract

Deep learning-based synthetic CT from black-bone MRI with limited retrospective clinical data for MR-only treatment planning

Primary: Analysis Methods - Image Synthesis and Translation
Secondary: Acquisition & Reconstruction - Quantitative Imaging: chest, abdomen, and pelvis
561-01-006 · Generative Models for Image Synthesis · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row B
Keywords: Radiotherapy Machine Learning/Artificial Intelligence Pelvis Synthetic CT Oncology Imaging
Accepted
Merlin Owens 1,2, N. Jane Taylor2, Andrew King1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
2Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, United Kingdom
Presenting Author: Merlin Owens

Synopsis

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References

1. Han, X. (2017) ‘MR-based synthetic CT generation using a deep convolutional neural network method’, Medical Physics, 44(4), pp. 1408–1419. doi:10.1002/mp.12155. [doi]
2. Leynes, A.P. et al. (2017) ‘Zero-echo-time and Dixon Deep Pseudo-ct (Zedd CT): Direct generation of Pseudo-CT images for pelvic pet/MRI attenuation correction using deep convolutional neural networks with Multiparametric MRI’, Journal of Nuclear Medicine, 59(5), pp. 852–858. doi:10.2967/jnumed.117.198051. [doi]
3. Wang, Y. et al. (2019) ‘Synthetic CT generation based on T2 weighted MRI of nasopharyngeal carcinoma (NPC) using a deep convolutional neural network (DCNN)’, Frontiers in Oncology, 9. doi:10.3389/fonc.2019.01333. [doi]
4. Getzmann, J.M. et al. (2024) ‘Deep learning-based pseudo-CT synthesis from Zero Echo time mr sequences of the pelvis’, Insights into Imaging, 15(1). doi:10.1186/s13244-024-01751-3. [doi]
5. Weiger, M., Pruessmann, K.P. and Hennel, F. (2011) ‘MRI with Zero echo time: Hard versus sweep pulse excitation’, Magnetic Resonance in Medicine, 66(2), pp. 379–389. doi:10.1002/mrm.22799. [doi]
6. Jerban, S. et al. (2020) ‘An update in qualitative imaging of bone using ultrashort echo time magnetic resonance’, Frontiers in Endocrinology, 11. doi:10.3389/fendo.2020.555756. [doi]
7. Eley, K.A. et al. (2012) ‘“black bone” MRI: A partial flip angle technique for radiation reduction in Craniofacial Imaging’, The British Journal of Radiology, 85(1011), pp. 272–278. doi:10.1259/bjr/95110289. [doi]
8. Goodfellow, I., Bengio, Y. and Courville, A. (2017) Deep learning. Cambridge, MA: The MIT Press.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-01-006