Fully Automated Image-Based Multiplexing of Serial PET/CT Imaging for Facilitating Comprehensive Disease Phenotyping.
Authors
Affiliations (14)
Affiliations (14)
- Digital Transformation in Radiology (DIGIT-X) Lab, Department of Radiology, Ludwig Maximilian University of Munich, Munich, Germany; [email protected].
- Quantitative Imaging and Medical Physics Team, Medical University of Vienna, Vienna, Austria.
- Department of Radiology, University of California-Davis, Davis, California.
- Department of Nuclear Medicine, West German Cancer Center, DKTK and NCT site, University Hospital Essen, Essen, Germany.
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
- Division of Nuclear Medicine, University of Tuebingen, Tuebingen, Germany.
- Division of Nuclear Medicine, Guilin Medical University, Guilin, China.
- Division of Nuclear Medicine, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
- Department of Medicine II (Oncology, Gastroenterology, Hepatology and Respiratory Medicine), University of Leipzig Medical Center, Leipzig, Germany.
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany.
- Faculty of Medicine & Health, University of Sydney, Sydney, New South Wales, Australia.
- Precision Molecular Imaging and Theranostics, Melbourne Theranostics Innovation Centre, Melbourne, Victoria, Australia; and.
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.
Abstract
Combined PET/CT imaging provides critical insights into both anatomic and molecular processes, yet traditional single-tracer approaches limit multidimensional disease phenotyping; to address this, we developed the PET Unified Multitracer Alignment (PUMA) framework-an open-source, postprocessing tool that multiplexes serial PET/CT scans for comprehensive voxelwise tissue characterization. <b>Methods:</b> PUMA utilizes artificial intelligence-based CT segmentation from multiorgan objective segmentation to generate multilabel maps of 24 body regions, guiding a 2-step registration: affine alignment followed by symmetric diffeomorphic registration. Tracer images are then normalized and assigned to red-green-blue channels for simultaneous visualization of up to 3 tracers. The framework was evaluated on longitudinal PET/CT scans from 114 subjects across multiple centers and vendors. Rigid, affine, and deformable registration methods were compared for optimal coregistration. Performance was assessed using the Dice similarity coefficient for organ alignment and absolute percentage differences in organ intensity and tumor SUV<sub>mean</sub> <b>Results:</b> Deformable registration consistently achieved superior alignment, with Dice similarity coefficient values exceeding 0.90 in 60% of organs while maintaining organ intensity differences below 3%; similarly, SUV<sub>mean</sub> differences for tumors were minimal at 1.6% ± 0.9%, confirming that PUMA preserves quantitative PET data while enabling robust spatial multiplexing. <b>Conclusion:</b> PUMA provides a vendor-independent solution for postacquisition multiplexing of serial PET/CT images, integrating complementary tracer data voxelwise into a composite image without modifying clinical protocols. This enhances multidimensional disease phenotyping and supports better diagnostic and therapeutic decisions using serial multitracer PET/CT imaging.