Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
531-02-009 / 531-02-009 ISMRM Abstract

When brains disagree: data ambiguity underlies the challenge of amyloid PET synthesis from structural MRI

Primary: Neuro - Alzheimer's Disease
Secondary: Analysis Methods - Image Synthesis and Translation
531-02-009 · Imaging Neurodegeneration in Motion: Multimodal Biomarkers for Alzheimer’s Disease and Parkinson’s Disease · Wednesday, 13 May, 1:40 PM–3:16 PM · Roof Terrace
531-02-009 · Imaging Neurodegeneration in Motion: Multimodal Biomarkers for Alzheimer’s Disease and Parkinson’s Disease · Wednesday, 13 May, 1:40 PM–3:16 PM · Roof Terrace
Keywords: Alzheimer's Disease Synthetic data Image translation Amyloid PET
Accepted
Louise E Baron 1,2, Ross Callaghan3, David M Cash4, Philip S Weston4, Hojjat Azadbakht3, Gary Zhang1,5
1UCL Hawkes Institute, University College London, London, United Kingdom
2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
3AINOSTICS Ltd, Manchester, United Kingdom
4Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, United Kingdom
5Department of Computer Science, University College London, London, United Kingdom
Presenting Author: Louise E Baron

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References

1. F. Vega, A. Addeh, A. Ganesh, E. E. Smith, and M. E. MacDonald, “Image translation forestimating two-dimensional axial amyloid-beta pet from structural mri,” Journal of Magnetic Resonance Imaging, vol. 59, pp. 1021–1031, 3 Mar. 2024, ISSN: 1053-1807. DOI: 10.1002/jmri.29070. [doi]
2. B. Theodorou, A. Dadu, B. Avants, M. Nalls, J. Sun, and F. Faghri, Mri2pet: Realistic pet image synthesis from mri for automated inference of brain atrophy and Alzheimer’s, Apr. 2025. DOI: 10.1101/2025.04.23.25326302. [doi]
3. A. Sargood, L. Puglisi, J. H. Cole, N. P. Oxtoby, D. Ravì, and D. C. Alexander, “Cocolit: Controlnet-conditioned latent image translation for mri to amyloid pet synthesis,” Aug. 2025. arXiv: 2508.01292
4. Z. Ou, Y. Pan, F. Xie, Q. Guo, and D. Shen, “Image-and-label conditioning latent diffusion model: Synthesizing aβ-pet from mri for detecting amyloid status,” IEEE Journal of Biomedical and Health Informatics, vol. 29, pp. 1221–1231, 2 Feb. 2025, ISSN: 2168-2194. DOI: 10.1109/JBHI.2024.3492020. [doi]
5. Y. Jin et al., “Brain mri to pet synthesis and amyloid estimation in Alzheimer’s disease via 3d multimodal contrastive gan,” in 2024, pp. 94–103. DOI: 10.1007/978-3-031-45673-2_10. [doi]
6. C. R. Jack et al., “Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s association workgroup,” Alzheimer’s Dementia, vol. 20, pp. 5143–5169, 8 Aug. 2024, ISSN: 1552-5260. DOI: 10.1002/alz.13859. [doi]
7. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” IEEE, Jul. 2017, pp. 5967–5976, ISBN: 978-1-5386-0457-1. DOI: 10.1109/CVPR.2017.632. [doi]
8. J. Lee, A. Murphy, T. Ward, T. Harrison, S. Landau, and W. Jagust, Amyloid pet processing methods, 2023.
9. G. Mårtensson et al., “Avra: Automatic visual ratings of atrophy from mri images using recurrent convolutional neural networks,” NeuroImage: Clinical, vol. 23, p. 101 872, 2019, ISSN:22131582. DOI: 10.1016/j.nicl.2019.101872. [doi]

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http://echo.ismrm.org/p/ISMRM2026/531-02-009