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Related Experiment Video

Updated: Jul 2, 2026

Automated HPLC Separation Using LC-Mate: An Integrated Repetitive Autosampler and Fraction Collector for Microscale Purification
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Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

Yufei Jin, Hengjia Ran, Gaoning Ning

    IEEE Transactions on Medical Imaging
    |June 30, 2026
    PubMed
    Summary
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    We developed a novel deep learning method for simultaneous dual-tracer PET imaging. This approach accurately separates tracer signals without needing paired single-tracer scans, improving clinical robustness and scalability.

    Area of Science:

    • Nuclear Medicine
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Simultaneous dual-tracer PET offers enhanced diagnostic information but faces challenges in separating hybrid signals.
    • Current deep learning (DL) methods require extensive, precisely aligned datasets, limiting clinical application.
    • Data acquisition for dual-tracer PET is costly and time-consuming due to alignment requirements.

    Purpose of the Study:

    • To introduce a self-supervised deep learning method for simultaneous dual-tracer PET signal separation.
    • To overcome the limitations of existing methods by eliminating the need for single-tracer scan labels and precise alignment.
    • To improve the clinical robustness and scalability of dual-tracer PET imaging.

    Main Methods:

    • Developed a simultaneous dual-tracer PET self-supervised separation (DTPSS) method guided by kinetic prior information.

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  • Integrated Ordinary Differential Equations (ODEs) residual constraints from a two-compartment model into the loss function.
  • Validated DTPSS on two large datasets (2,400 brain image samples) and in animal studies (rats and mice).

    • Main Results:

    • DTPSS demonstrated superior quantitative and qualitative performance compared to supervised and other self-supervised methods.
    • The method showed robustness across different tracer combinations and sampling protocols.
    • In animal studies, DTPSS achieved improved separation even with physiological variability and unavailable paired labels.

    Conclusions:

    • DTPSS enables accurate and robust dual-tracer separation in PET imaging without requiring single-tracer labels.
    • The kinetic prior-guided self-supervised approach enhances clinical applicability and scalability of simultaneous dual-tracer PET.
    • This method holds significant potential for advancing nuclear medicine diagnostics.