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PET Image Reconstruction With Kernel and Kernel Space Composite Regularizer.

Shiyao Guo, Yuxia Sheng, Li Chai

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    This study introduces a novel regularized kernelized expectation maximization (RKEM) method for positron emission tomography (PET) image reconstruction. RKEM improves image quality by simultaneously preserving details and reducing variance, outperforming existing methods.

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    Area of Science:

    • Medical Imaging
    • Image Reconstruction
    • Nuclear Medicine

    Background:

    • Kernelized expectation maximization (KEM) methods are prominent in PET image reconstruction but suffer from high variance and sensitivity to iteration.
    • Existing methods struggle to simultaneously preserve image details and suppress variance.

    Purpose of the Study:

    • To develop a novel regularized KEM (RKEM) method for improved PET image reconstruction.
    • To address limitations of KEM, including reconstruction variance and detail preservation.

    Main Methods:

    • Developed a novel regularized KEM (RKEM) method incorporating a kernel space composite regularizer.
    • The regularizer combines convex graph smoothing and concave energy enhancement for kernel coefficients.
    • Utilized optimization transfer for a globally convergent iterative algorithm for RKEM reconstruction.

    Main Results:

    • The proposed RKEM method demonstrated superior performance compared to KEM and conventional methods.
    • Evaluations on simulated and in vivo data validated the algorithm's effectiveness.
    • The composite regularizer facilitates the use of PET-only image priors, overcoming KEM's limitations.

    Conclusions:

    • The novel RKEM method offers significant advantages for PET image reconstruction.
    • RKEM effectively balances image detail preservation and variance suppression.
    • This approach enhances the utility of KEM by enabling the use of PET-specific image priors.