Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
663-02-011 ISMRM Abstract

The effect of T1-weighted MRI guidance on domain randomized Image Quality Transfer of DTI

Primary: Analysis Methods - Image Enhancement
Secondary: Diffusion - DWI/DTI/DKI
663-02-011 · Diffusion: DWI/DTI/DKI · Thursday, 14 May, 9:25 AM–10:20 AM · Digital Posters Row D
Keywords: Multimodal Diffusion MRI Super Resolution Domain Randomization
Accepted
Alp G Cicimen 1, Henry F J Tregidgo1, Matteo Figini1, Eirini Messaritaki2, Carolyn McNabb2, Marco Palombo3,4, John Evans2,5,6,7, Mara Cercignani2, Derek K Jones2, Daniel C Alexander1
1Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
2Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
3CUBRIC, Cardiff University, Cardiff, United Kingdom
4School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
5Cardiff University, Cardiff, United Kingdom
6School of Psychology, Cardiff University, Cardiff, United Kingdom
7School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
Presenting Author: Alp G Cicimen

Synopsis

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References

1. Alexander, D.C. et al.: Image quality transfer and applications in diffusion mri. NeuroImage 152, 283–298 (2017)
2. Tanno, R. et al. Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution. In Medical Image Computing and Computer Assisted Intervention−MICCAI 2017. pp. 611-619. Springer International Publishing.
3. Cicimen, A. G. et al. Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI. In International Workshop on Computational Diffusion MRI, 106-118 (2024)
4. J. Tobin, R. et al. Domain randomization for transferring deep neural networks from simulation to the real world. In IEEE/RSJ International Conference on Intelligent Robots and Systems–IROS, 23-30 (2017)
5. Gopinath, K. et al. Synthetic data in generalizable, learning-based neuroimaging. Imaging Neuroscience (2024)
6. Van Essen, D. et al. The human connectome project: A data acquisition perspective. NeuroImage 62(4), 2222–2231 (2012)
7. McNabb, C.B., et al. WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis. Sci Data 12, 220 (2025)
8. Basser, P.J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance, Series B, 111, 209-219 (1996)
9. Wang, Z., Bovik, A. A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)
10. Pajevic, S., Pierpaoli, C.: Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magnetic Resonance in Medicine 43(6), 921–921 (2000)
11. Jones, D.K., Knösche, T.R., Turner, R.: White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion mri. NeuroImage 73, 239–254 (2013)
12. Özarslan, E., Koay, C.G., Shepherd, T.M., Komlosh, M.E., İrfanoğlu, M.O., et al.: Mean apparent propagator (map) mri: A novel diffusion imaging method for mapping tissue microstructure. NeuroImage 78, 16–32 (2013)

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/663-02-011