Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
368-01-003 ISMRM Abstract

DeepSUM-DWI: A combined registration and super-resolution model for diffusion-weighted imaging

Primary: Analysis Methods - Image Enhancement
Secondary: Acquisition & Reconstruction - AI methods
368-01-003 · Registration, Atlases, and Motion · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row I
Keywords: Diffusion weighted imaging CONVOLUTIONAL NEURAL NETWORK Super Resolution Deep learning image registration Deep neural networks
Accepted
Merlin J Fair 1, Carolina Daniells Zaldívar2, Paola Ocampo Luna2, Luis Concha1, María Guadalupe García-Gomar2
1Instituto de Neurobiología, Universidad Nacional Autonoma de Mexico, Queretaro, Mexico
2Escuela Nacional de Estudios Superiores Juriquilla, Universidad Nacional Autonoma de Mexico, Queretaro, Mexico
Presenting Author: Merlin J Fair

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References

1. D. C. Lepcha, B. Goyal, A. Dogra, and V. Goyal, “Image super-resolution: A comprehensive review, recent trends, challenges and applications,” Inf. Fusion, vol. 91, no. C, pp. 230–260, Mar. 2023, doi: 10.1016/j.inffus.2022.10.007. [doi]
2. Z. Ji et al., “Deep learning-based magnetic resonance image super-resolution: a survey,” Neural Comput & Applic, vol. 36, no. 21, pp. 12725–12752, July 2024, doi: 10.1007/s00521-024-09890-w. [doi]
3. A. Muhammad, S. Aramvith, K. Duangchaemkarn, and M.-T. Sun, “Brain MRI Image Super-Resolution Reconstruction: A Systematic Review,” IEEE Access, vol. 12, pp. 156347–156362, 2024, doi: 10.1109/ACCESS.2024.3478829. [doi]
4. K. Matsuo et al., “Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images,” Neuroradiology, vol. 65, no. 11, pp. 1619–1629, Nov. 2023, doi: 10.1007/s00234-023-03212-y. [doi]
5. S. Luo, J. Zhou, Z. Yang, H. Wei, and Y. Fu, “Diffusion MRI super-resolution reconstruction via sub-pixel convolution generative adversarial network,” Magn Reson Imaging, vol. 88, pp. 101–107, May 2022, doi: 10.1016/j.mri.2022.02.001. [doi]
6. S. Altmann et al., “Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing,” Acad Radiol, vol. 31, no. 10, pp. 4171–4182, Oct. 2024, doi: 10.1016/j.acra.2024.02.049. [doi]
7. A. Ordinola, D. Abramian, M. Herberthson, A. Eklund, and E. Özarslan, “Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning,” Sci Rep, vol. 15, no. 1, p. 6580, Feb. 2025, doi: 10.1038/s41598-025-90972-7. [doi]
8. A. B. Molini, D. Valsesia, G. Fracastoro, and E. Magli, “DeepSUM: Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3644–3656, May 2020, doi: 10.1109/TGRS.2019.2959248. [doi]
9. F. Wang et al., “In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution,” Sci Data, vol. 8, no. 1, p. 122, Apr. 2021, doi: 10.1038/s41597-021-00904-z. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/368-01-003