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

TL;DR

This paper introduces a physics-informed CycleGAN approach for rapid, non-invasive voxel-wise kinetic modeling in dynamic PET, reducing reliance on invasive arterial input function estimation and producing accurate parameter maps.

Contribution

It applies a physics-informed CycleGAN to dynamic PET data, enabling fast, voxel-wise kinetic modeling without invasive AIF estimation, which is a novel adaptation of MRI techniques.

Findings

Accurate AIF predictions matching reference data

High-quality parameter maps produced

Demonstrated potential for clinical application

Abstract

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.