Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-04-004 ISMRM Abstract

Reconstructing High-Resolution Tau Distributions from Regional tau-PET Data Using Implicit Neural Representations

Primary: Physics & Engineering - PET/MR
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
664-04-004 · Image Reconstruction: Self-Supervised and Implicit · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row E
Keywords: Alzheimer's Disease MRI-derived Atrophy Tau-PET Voxel-level Reconstruction Implicit Neural Representation (INR)
Accepted
Anil Kamat1, Daren Ma1, Ashish Raj 2,3,4
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, United States of America
2Radiology and Biomedical Imaging, University Of California, San Francisco (UCSF), United States of America
3Department of Radiology and Biomedical Imaging, University Of California, San Francisco (UCSF), United States of America
4Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, United States of America
Presenting Author: Ashish Raj

Synopsis

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References

1. Hall, B., Mak, E., Cervenka, S., Aigbirhio, F. I., Rowe, J. B., & O’Brien, J. T. (2017). In vivo tau PET imaging in dementia: pathophysiology, radiotracer quantification, and a systematic review of clinical findings. Ageing research reviews, 36, 50-63.
2. Molaei, A., Aminimehr, A., Tavakoli, A., Kazerouni, A., Azad, B., Azad, R., & Merhof, D. (2023). Implicit neural representation in medical imaging: A comparative survey. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2381-2391).
3. Karlsson, L., Vogel, J., Arvidsson, I., Åström, K., Strandberg, O., Seidlitz, J., ... & Hansson, O. (2025). Machine learning prediction of tau‐PET in Alzheimer's disease using plasma, MRI, and clinical data. Alzheimer's & Dementia, 21(2), e14600.
4. Lee, J., Burkett, B. J., Min, H. K., Senjem, M. L., Dicks, E., Corriveau-Lecavalier, N., ... & Jones, D. T. (2024). Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain, 147(3), 980-995.
5. Zou, J., Park, D., Johnson, A., Feng, X., Pardo, M., France, J., ... & Alzheimer's Disease Neuroimaging Initiative. (2021). Deep learning improves utility of tau PET in the study of Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12264.
6. Lehmann, M., Douiri, A., Kim, L. G., Modat, M., Chan, D., Ourselin, S., ... & Fox, N. C. (2010). Atrophy patterns in Alzheimer's disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements. Neuroimage, 49(3), 2264-2274.
7. Mak, E., Bethlehem, R. A., Romero-Garcia, R., Cervenka, S., Rittman, T., Gabel, S., ... & O'Brien, J. T. (2018). In vivo coupling of tau pathology and cortical thinning in Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 678-687.
8. Ossenkoppele, R., Schonhaut, D. R., Schöll, M., Lockhart, S. N., Ayakta, N., Baker, S. L., ... & Rabinovici, G. D. (2016). Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain, 139(5), 1551-1567.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/664-04-004