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    A novel deep learning method enhances gamma-ray CT (gCT) image reconstruction for PET-enabled dual-energy CT (DECT). This single-subject approach improves image quality and material decomposition without extensive training data.

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    Area of Science:

    • Medical Imaging
    • Radiology
    • Computational Imaging

    Background:

    • Dual-energy computed tomography (DECT) combined with positron emission tomography (PET) has clinical potential but faces hardware and radiation dose challenges.
    • Existing PET-enabled DECT methods reconstruct a gamma-ray CT (gCT) image from PET data but haven't fully utilized prior knowledge.
    • Deep learning offers promise but typically requires large datasets, impractical for novel methods like PET-enabled DECT.

    Purpose of the Study:

    • To develop a single-subject deep learning method for improved gCT image reconstruction in PET-enabled DECT.
    • To enhance gCT image quality and multi-material decomposition without population-based pre-training.
    • To address the limitations of existing kernelized reconstruction frameworks by incorporating deep coefficient priors.

    Main Methods:

    • Proposed a novel neural network representation as a deep coefficient prior for gCT image reconstruction.
    • Developed a single-subject method that avoids population-based pre-training.
    • Employed an optimization transfer strategy with quadratic surrogates to solve the complex tomographic estimation problem.
    • Implemented an iterative algorithm including PET activity update, gCT image update, and least-squares neural network learning.

    Main Results:

    • The proposed method significantly improved gCT image quality compared to existing techniques.
    • Enhanced multi-material decomposition was achieved using the improved gCT images.
    • Validation demonstrated effectiveness across computer simulations, phantom data, and patient data.

    Conclusions:

    • The single-subject deep learning approach effectively improves gCT image reconstruction for PET-enabled DECT.
    • This method overcomes the need for large training datasets, making deep learning more accessible for new imaging modalities.
    • The technique shows significant potential for advancing quantitative imaging and material decomposition in hybrid PET/CT systems.