# Fast Voxel-Wise Kinetic Modeling in Dynamic PET using a Physics-Informed CycleGAN

arXiv: https://arxiv.org/abs/2510.23140 · HTML: https://mlsanity.com/paper/2510.23140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 27 Oct 2025.

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

- [Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [18F]FDG PET Images](https://mlsanity.com/paper/2510.21281/) (2025)

## Abstract (arXiv)

Tracer kinetic modeling serves a vital role in diagnosis, treatment planning, tracer development and oncology, but burdens practitioners with complex and invasive arterial input function estimation (AIF). We adopt a physics-informed CycleGAN showing promise in DCE-MRI quantification to dynamic PET quantification. Our experiments demonstrate sound AIF predictions and parameter maps closely resembling the reference.