Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-007 / 452-03-007 ISMRM Abstract

A pan-contrast, pan-resolution super-resolution model for brain MRI across the human lifespan

Primary: Analysis Methods - Image Enhancement
Secondary: Neuro - Development
452-03-007 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-007 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Deep learning Brain Development MRI Super Resolution
Accepted
Johnathan C Leung 1, Xiaoyang Chen2, Chengche Tsai1, Khoi M Huynh2, Kim-Han Thung2, Rhea Adams3,4, Sahar Ahmad2, Dan Ma4,5, Pew-Thian Yap2
1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, United States of America
3Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States of America
4Department of Biomedical Engineering, Duke University, Durham, United States of America
5Department of Neurosurgery, Duke University School of Medicine, Durham, United States of America
Presenting Author: Johnathan C Leung

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Tsai CC, Chen X, Ahmad S, Yap PT. Unsupervised single-image super-resolution for infant brain MRI. Neuroimage. 2025;317:121293. doi:10.1016/j.neuroimage.2025.121293. [doi]
2. Pham CH, Tor-Díez C, Meunier H, Bednarek N, Fablet R, Passat N, Rousseau F. Multiscale brain MRI super-resolution using deep 3D convolutional networks. Comput Med Imaging Graph. 2019;77:101647. doi:10.1016/j.compmedimag.2019.101647. [doi]
3. Iglesias JE, Billot B, Balbastre Y, Magdamo C, Arnold SE, Das S, Edlow BL, Alexander DC, Golland P, Fischl B. SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry. Sci Adv. 2023;9(5):eadd3607. doi:10.1126/sciadv.add3607. [doi]
4. Wang J, Levman J, Pinaya WHL, Tudosiu P-D, Cardoso MJ, Marinescu R. InverseSR: 3D brain MRI super-resolution using a latent diffusion model. In: Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv.; 2023. p. 438–47.
5. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv.; 2015. p. 234–41.
6. Adams R, Zhao W, Hu S, Lyu W, Huynh KM, Ahmad S, Ma D, Yap P-T. Contrast-agnostic MRI super-resolution using domain randomization. bioRxiv. 2024 Dec 5;2024.12.05.627056. doi:10.1101/2024.12.05.627056. [doi]
7. Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014.
8. Howell BR, Styner MA, Gao W, Yap PT, Wang L, Baluyot K, Yacoub E, Chen G, Potts T, Salzwedel A, Li G, Gilmore JH, Piven J, Smith JK, Shen D, Ugurbil K, Zhu H, Lin W, Elison JT. The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. Neuroimage. 2019 Jan 15;185:891–905. doi:10.1016/j.neuroimage.2018.03.049. [doi]
9. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K; WU-Minn HCP Consortium. The WU-Minn Human Connectome Project: an overview. NeuroImage. 2013;80:62–79.
10. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004 Apr;13(4):600–12. doi:10.1109/TIP.2003.819861. [doi]
11. Xue W, Zhang L, Mou X, Bovik AC. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans Image Process. 2014 Feb;23(2):684–95. doi:10.1109/TIP.2013.2293423. [doi]
12. van den Broek R, Lena B, Webb A. Paired 64 mT and 3 T Brain MRI Scans of Healthy Subjects for Neuroimaging Research [Data set]. Zenodo; 2025. doi:10.5281/zenodo.15374450. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/452-03-007