Fast Voxel-Wise Kinetic Modeling in Dynamic PET using a Physics-Informed CycleGAN
This work addresses a bottleneck in tracer kinetic modeling for medical imaging practitioners, but it is incremental as it adapts an existing method from DCE-MRI to dynamic PET.
The paper tackled the problem of complex and invasive arterial input function estimation in dynamic PET kinetic modeling by adopting a physics-informed CycleGAN, achieving sound AIF predictions and parameter maps closely resembling the reference.
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.