Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-012 / 452-03-012 ISMRM Abstract

Enhancement of Mid-Field (0.6T) T2-Weighted Prostate Scans Using a Two-Stage Refinement Framework

Primary: Analysis Methods - Image Enhancement
Secondary: Physics & Engineering - Low-Field MRI
452-03-012 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-012 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Image Quality Enhancement 0.6t Two-stage refinement Flow matching Mid-field Prostate MRI
Accepted
Donghang Lyu 1, Martijn Nagtegaal2, Ece Ercan3, Zhong Li4, Mart WJ van Straten2,5, Marius Staring1, Andrew Webb2, Matthias van Osch2, Peter Börnert2,6, Yiming Dong2
1Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
2Department of Radiology, C.J. Gorter MRI Center, Leiden University Medical Center, Netherlands
3Philips Healthcare, Best, Netherlands
4Great Bay University, Dongguan, China
5Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
6Philips Innovative Technologies, Hamburg, Germany
Presenting Author: Donghang Lyu

Synopsis

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References

1. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer; 2015. p 234–241.
2. Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., & Le, M. Flow Matching for Generative Modeling. In The Eleventh International Conference on Learning Representations.
3. Tibrewala, R., Dutt, T., Tong, A., Ginocchio, L., Lattanzi, R., Keerthivasan, M. B., ... & Johnson, P. M. (2024). FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging. Scientific data, 11(1), 404.
4. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
5. Chen, Z., Pawar, K., Ekanayake, M., Pain, C., Zhong, S., & Egan, G. F. (2023). Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges. Journal of Digital Imaging, 36(1), 204-230.
6. Ma, Q., Meng, Q., Qiao, M., Matthews, P. M., O’Regan, D. P., & Bai, W. (2025, September). CardiacFlow: 3D+ t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 89-99). Cham: Springer Nature Switzerland.
7. Ayaz, A., Boonstoppel, R., Lorenz, C., Weese, J., Pluim, J., & Breeuwer, M. (2024). Effective deep-learning brain MRI super resolution using simulated training data. Computers in Biology and Medicine, 183, 109301.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/452-03-012