# Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET

arXiv: https://arxiv.org/abs/2305.10569 · HTML: https://mlsanity.com/paper/2305.10569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 1 of 1)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)

## Built on by (most cited first, 4 of 4)

- [Physiological neural representation for personalised tracer kinetic parameter estimation from dynamic PET](https://mlsanity.com/paper/2504.17122/) (2025)
- [Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging](https://mlsanity.com/paper/2405.21021/) (2024)
- [Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules](https://mlsanity.com/paper/2410.22674/) (2024)
- [Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization](https://mlsanity.com/paper/2605.28164/) (2026)

## Abstract (arXiv)

Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes. Voxel-wise physiologically-based pharmacokinetic (PBPK) modeling of the time activity curves (TAC) can provide relevant diagnostic information for clinical workflow. Conventional fitting strategies for TACs are slow and ignore the spatial relation between neighboring voxels. We train a spatio-temporal UNet to estimate the kinetic parameters given TAC from F-18-fluorodeoxyglucose (FDG) dPET. This work introduces a self-supervised loss formulation to enforce the similarity between the measured TAC and those generated with the learned kinetic parameters. Our method provides quantitatively comparable results at organ-level to the significantly slower conventional approaches, while generating pixel-wise parametric images which are consistent with expected physiology. To the best of our knowledge, this is the first self-supervised network that allows voxel-wise computation of kinetic parameters consistent with a non-linear kinetic model. The code will become publicly available upon acceptance.