531-02-001 / 531-02-001 ISMRM Abstract

A Multistep-Multitask Approach to Reconstruct Tau PET scans of AD Subjects From Simpler Radiology Inputs

Primary: Neuro - Alzheimer's Disease

Secondary: Analysis Methods - Classification and Prediction

531-02-001 · 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 Deep learning 3D MRI Tau pet Event-based model

Accepted

Daren Ma¹, Robin Sandell², Ashish Raj ³

¹Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States of America

²Department of Radiology and Biomedical Imaging, University Of California, San Francisco (UCSF), United States of America

³Radiology and Biomedical Imaging, University Of California, San Francisco (UCSF), United States of America

Presenting Author: Ashish Raj

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References

1. 1. Ossenkoppele R, van der Kant R, Hansson O. Tau biomarkers in Alzheimer's disease: towards implementation in clinical practice and trials. Lancet Neurol. 2022; 21: 726-734. doi:10.1016/S1474-4422(22)00168-5 [doi]

2. 2. Lee J, Burkett BJ, Min H-K, et al. Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain. 2024; 147: 980-995. doi:10.1093/brain/awad346 [doi]

3. 3. Chen KT, Tesfay R, Koran MEI, et al. Generative adversarial network-enhanced ultra-low-dose [18F]-PI-2620 s PET/MRI in aging and neurodegenerative populations. Am J Neuroradiol. 2023; 44: 1012-1020. doi:10.3174/ajnr.A7961 [doi]

4. 4. Karlsson L, Vogel J, Arvidsson I, et al. Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data. Alzheimer's Dement. 2025; 21:e14600. https://doi.org/10.1002/alz.14600 [doi]

5. 5. Sandell R, Torok J, Ranasinghe KG, Nagarajan SS, Raj A. Back to the Future: Predicting Individual Tau Progression in Alzheimer's Disease. Res Sq [Preprint]. 2025 Jun 19:rs.3.rs-6772220. doi: 10.21203/rs.3.rs-6772220/v1. PMID: 40585245; PMCID: PMC12204357. [doi] [pmid]

6. 6. Ma D, Pabalan C, RajagopalA, Akanksha A, Interian Y, Yang Y, Raj A. Multi-task Learning and Ensemble Approach to Predict Cognitive Scores for Patients with Alzheimer’s Disease bioRxiv 2021.12.08.471856; doi: https://doi.org/10.1101/2021.12.08.471856 [doi]

7. 7. Aksman LM, Wijeratne PA, Oxtoby NP, Eshaghi A, Shand C, Altmann A, Alexander DC, Young AL. pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm. SoftwareX. 2021 Dec;16:100811. doi: 10.1016/j.softx.2021.100811. Epub 2021 Sep 25. PMID: 34926780; PMCID: PMC8682799. [doi] [pmid]

8. 8. Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease. Cell Rep. 2015 Jan 20;10(3):359-369. doi: 10.1016/j.celrep.2014.12.034. Epub 2015 Jan 15. PMID: 25600871; PMCID: PMC5747552. [doi] [pmid]

9. 9. Anand, C., Maia, P.D., Torok, J., Mezias, C. and Raj, A. (2021), The effect of microglial genes on network diffusion of pathology in mouse models of tauopathy. Alzheimer's Dement., 17: e052382. https://doi.org/10.1002/alz.052382 [doi]

10. 10. Yu X, Torok J, Pandya S, et al. Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations[J]. arXiv preprint arXiv:2506.11158, 2025. https://doi.org/10.48550/arXiv.2506.11158 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/531-02-001