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PET Head Motion Estimation Using Supervised Deep Learning With Attention.

Zhuotong Cai, Tianyi Zeng, Jiazhen Zhang

    IEEE Transactions on Medical Imaging
    |October 13, 2025
    PubMed
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    Deep learning head motion correction (DL-HMC++) accurately predicts head movement in brain PET scans using raw data. This method significantly reduces motion artifacts, improving image quality and quantitative analysis for neurological disorder diagnosis.

    Area of Science:

    • Medical Imaging
    • Neuroscience
    • Artificial Intelligence

    Background:

    • Head movement in brain PET imaging causes artifacts and quantification errors.
    • Hardware-based motion tracking (HMT) is not always practical in clinical settings.

    Purpose of the Study:

    • To develop and evaluate a deep learning approach (DL-HMC++) for head motion correction in PET imaging.
    • To enable accurate quantitative analysis and diagnosis of neurological disorders.

    Main Methods:

    • A deep learning model (DL-HMC++) was trained using supervised learning on dynamic PET scans with HMT data.
    • The model predicts rigid head motion from one-second 3D PET raw data.
    • Evaluation was performed on two PET scanners and four radiotracers.

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    Main Results:

    • DL-HMC++ outperformed existing data-driven motion estimation methods.
    • Generated motion-free images with clear brain structure delineation and minimal artifacts.
    • Quantitative analysis showed minimal differences compared to gold-standard HMT (1.2±0.5% on HRRT, 0.5±0.2% on mCT).

    Conclusions:

    • DL-HMC++ offers effective and generalizable data-driven PET head motion correction.
    • This approach removes the need for HMT, making motion correction more accessible in clinical practice.
    • DL-HMC++ has the potential to improve diagnostic accuracy for neurological conditions.