{"ID":2849537,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23140","arxiv_id":"2510.23140","title":"Fast Voxel-Wise Kinetic Modeling in Dynamic PET using a Physics-Informed CycleGAN","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.","short_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 e...","url_abs":"https://arxiv.org/abs/2510.23140","url_pdf":"https://arxiv.org/pdf/2510.23140v1","authors":"[\"Christian Salomonsen\",\"Samuel Kuttner\",\"Michael Kampffmeyer\",\"Robert Jenssen\",\"Kristoffer Wickstrøm\",\"Jong Chul Ye\",\"Elisabeth Wetzer\"]","published":"2025-10-27T09:17:02Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"q-bio.OT\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}