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

2025/10/27 by Salomonsen, Christian, Kuttner, Samuel, Kampffmeyer, Michael +4
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Other Quantitative Biology (q-bio.OT)

paper · doi:10.48550/arxiv.2510.23140

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.

Citations

• Physics-Informed Deep Learning for Improved Input Function Estimation in Motion-Blurred Dynamic [¹⁸F]FDG PET Images

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