Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-018 / 452-03-018 ISMRM Abstract

No free lunch: how to address underdetermination in deep-learning-based image super-resolution?

Primary: Analysis Methods - Image Enhancement
Secondary: Acquisition & Reconstruction - AI methods
452-03-018 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-018 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Super-resolution Fractional anisotropy Image Quality Transfer U-Net
Accepted
Cosimo Campo 1,2, Hongxiang Lin3,4, John Ashburner5, Christian Lambert5, Harith Akram6,7, Gary Zhang2,8
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
2Hawkes Institute, University College London, London, United Kingdom
3Medical Engineering & Engineering Medicine Innovation Center, Hangzhou International Innovation Institute, Beihang University, Beijing, China
4Key Laboratory of Biomechanics and Mechanobiology, Beihang University, Beijing, China
5Functional Imaging Laboratory, UCL Queen Square Institute of Neurology, London, United Kingdom
6Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
7UCL Functional Neurosurgery Unit, National Hospital for Neurology and Neurosurgery, London, United Kingdom
8Department of Computer Science, University College London, London, United Kingdom
Presenting Author: Cosimo Campo

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References

1. D. Qiu, Y. Cheng, and X. Wang, ‘Medical image super-resolution reconstruction algorithms based on deep learning: A survey’, Computer Methods and Programs in Biomedicine, vol. 238, p. 107590, Aug. 2023, doi:10.1016/j.cmpb.2023.107590. [doi]
2. D. C. Alexander et al., ‘Image quality transfer and applications in diffusion MRI’, NeuroImage, vol. 152, pp. 283–298, May 2017, doi:10.1016/j.neuroimage.2017.02.089. [doi]
3. J. E. Iglesias et al., ‘SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry’, Sci Adv, vol. 9, no. 5, p. eadd3607, Feb. 2023, doi:10.1126/sciadv.add3607. [doi]
4. A. G. Cicimen et al., ‘Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI’, in Computational Diffusion MRI, M. Chamberland, T. Hendriks, M. Karaman, R. Mito, N. Newlin, S. Shailja, and E. Thompson, Eds, Cham: Springer Nature Switzerland, 2025, pp. 106–118. doi:10.1007/978-3-031-86920-4_10. [doi]
5. Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, ‘3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation’, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, Eds, Cham: Springer International Publishing, 2016, pp. 424–432. doi:10.1007/978-3-319-46723-8_49. [doi]
6. M. F. Glasser et al., ‘The minimal preprocessing pipelines for the Human Connectome Project’, NeuroImage, vol. 80, pp. 105–124, Oct. 2013, doi:10.1016/j.neuroimage.2013.04.127. [doi]
7. M. Jenkinson, C. F. Beckmann, T. E. J. Behrens, M. W. Woolrich, and S. M. Smith, ‘FSL’, NeuroImage, vol. 62, no. 2, pp. 782–790, Aug. 2012, doi:10.1016/j.neuroimage.2011.09.015. [doi]

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http://echo.ismrm.org/p/ISMRM2026/452-03-018