Fully Automated Image-Based Multiplexing of Serial PET/CT Imaging for Facilitating Comprehensive Disease Phenotyping

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.

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Publication Details

Journal
Journal of Nuclear Medicine
Published
2025-09-18
DOI
https://doi.org/10.2967/jnumed.125.269688
Citations
3
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
4.08
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article

Fully Automated Image-Based Multiplexing of Serial PET/CT Imaging for Facilitating Comprehensive Disease Phenotyping

Osama Sabri, Lalith Kumar Shiyam Sundar, Hubert Wirtz, Philip Hasbak et al.
3 citations
Journal of Nuclear Medicine
Medical Imaging Techniques and Applications
4.08
article

Fully Automated Image-Based Multiplexing of Serial PET/CT Imaging for Facilitating Comprehensive Disease Phenotyping

Osama Sabri, Lalith Kumar Shiyam Sundar, Hubert Wirtz, Philip Hasbak, Sebastian Gutschmayer, Elisabetta Abenavoli, Martin Lyngby Lassen, David Lok Hang Chan, Pia Linder, Simon R. Cherry, David Kersting, Ramsey Derek Badawi, Moon Kim, Manuel Melo Pires, Rodney J. Hicks, Xingyu Mu, Roberto Sciagrà, Wolfgang P. Fendler, Yasser G. Abdelhafez, Jason Callahan, Armin Frille, Daria Ferrara, Zewen Jiang, Thomas Beyer, Dale L. Bailey, Swen Hesse, Thu Hien Nguyen, Fabian Schmidt, Benjamin A. Spencer
article en
3 citations

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.

Journal of Nuclear MedicineVol. 66(11)
University of Copenhagen (DK), The University of Sydney (AU), The University of Melbourne (AU), Guilin Medical University (CN), Rigshospitalet (DK), Bavarian Research Institute for Digital Transformation (DE), St Vincent's Hospital Melbourne (AU), Theranostics (New Zealand) (NZ), Azienda Ospedaliero-Universitaria Careggi (IT), Essen University Hospital (DE), University Hospital Leipzig (DE), Molecular Theranostics (United States) (US), West German Heart and Vascular Center Essen (DE), Medical University of Vienna (AT), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE), University of California, Davis (US), Leipzig University (DE)
Openalex Percentile: Top 6%
Medical Imaging Techniques and Applications
4.08
DOISourceOpenAlex
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