Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
363-05-009 ISMRM Abstract

Highly Accelerated Brain MRI Matches Routine Scans for AI Image Analysis Task Performance

Primary: Acquisition & Reconstruction - AI methods
Secondary: Neuro - Dementia
363-05-009 · Classification and Analysis in the Brain · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row D
Keywords: Abbreviated/Rapid MRI Foundation model Neurodegenerative Disease Classification Brain-Age Prediction Whole-Brain Segmentation
Accepted
Moona Mazher 1,2, Miguel Rosa-Grilo3, Haroon R Chughtai2,4,5, David L Thomas3,6, Millie Beament3, Frederik Barkhof2,3,7, Catherine J. Mummery3, Nick C. Fox3, Geoff J Parker2,5, Daniel C Alexander1,2
1Department of Computer Science, University College London, London, United Kingdom
2UCL Hawkes Institute, University College London, London, United Kingdom
3Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
4Advanced Research Computing Centre, University College London, London, United Kingdom
5Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
6Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
7Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
Presenting Author: Moona Mazher

Synopsis

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References

1. Rosa‐Grilo M, Chughtai HR, Thomas DL, Alexander DC, Beament M, Belder CR, Jäger HR, Lim EA, Magill N, Mallon D, Nicholas JM. Ultra‐fast MRI for dementia diagnosis and treatment eligibility: A prospective study. Alzheimer's & Dementia. 2025 Jun;21(6):e70341. PMID: 40501106, DOI: 10.1002/alz.70341 [doi] [pmid]
2. Moor M, Banerjee O, Abad ZS, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P. Foundation models for generalist medical artificial intelligence. Nature. 2023 Apr 13;616(7956):259-65. DOI: https://doi.org/10.1038/s41586-023-05881-4 [doi]
3. Mazher M, Alexander DC, Qayyum A, Niederer SA. Advancing medical image segmentation with self-supervised learning: a 3D student-teacher approach for cardiac and neurological imaging. In: Medical Imaging with Deep Learning 2025 [Internet]. [cited 2025 Oct 16]. Available from: https://openreview.net/forum?id=PBdGho6avn
4. He K, Chen X, Xie S, Li Y, Dollár P, Girshick R. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022 (pp. 16000-16009). DOI:10.1109/CVPR52688.2022.01553 [doi]
5. Munk A, Cerri S, Ambsdorf J, Machnio J, Llambias SN, Nersesjan V, Krag CH, Liu P, García PR, Ghazi MM, Boesen M. A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning. arXiv preprint arXiv:2506.14432. 2025 Jun 17. DOI: https://doi.org/10.48550/arXiv.2506.14432 [doi]
6. Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine. 2021 Oct 1;72. PMID: 34614461, DOI: 10.1016/j.ebiom.2021.103600 [doi] [pmid]
7. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention 2015 Oct 5 (pp. 234-241). Cham: Springer international publishing. DOI: https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
8. Billot B, Greve DN, Puonti O, Thielscher A, Van Leemput K, Fischl B, Dalca AV, Iglesias JE. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Medical image analysis. 2023 May 1;86:102789. PMID: 36857946, DOI: 10.1016/j.media.2023.102789 [doi] [pmid]
9. Fischl B. FreeSurfer. Neuroimage. 2012 Aug 15;62(2):774-81. PMID: 22248573, DOI: 10.1016/j.neuroimage.2012.01.021 [doi] [pmid]

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http://echo.ismrm.org/p/ISMRM2026/363-05-009