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    Area of Science:

    • Medical Imaging
    • Neuroscience
    • Biophysics

    Background:

    • Positron emission tomography (PET) enables brain scans in freely moving animals.
    • Motion correction is crucial but introduces motion-dependent blurring due to spatially variant point spread function (SVPSF).
    • Existing methods struggle with consistent resolution and quantification across diverse motion patterns.

    Purpose of the Study:

    • To develop a method for calculating image-based resolution kernels (MD-SVPSF) to correct motion-dependent blurring in PET scans.
    • To improve spatial resolution and quantitative accuracy in motion-corrected PET images of freely moving animals.
    • To achieve motion-corrected image consistency regardless of the animal's movement patterns.

    Main Methods:

    • Developed a method to compute motion-dependent and spatially variant point spread function (MD-SVPSF) resolution kernels.
    • Calculated kernels by averaging SVPSF across measured positions within the scanner's field of view (FOV).
    • Applied MD-SVPSF to motion-corrected reconstructions and compared with independent SVPSF and Gaussian kernels.

    Main Results:

    • MD-SVPSF improved spatial resolution and corrected parallax-induced image deformation in phantom scans.
    • The method demonstrated comparable performance to pose-specific SVPSF application but was significantly faster (over 100x).
    • In freely moving mice, MD-SVPSF enhanced correlation between motion-free and motion-corrected brain quantification compared to other methods.

    Conclusions:

    • The developed MD-SVPSF method effectively corrects spatial resolution loss in motion-corrected PET images.
    • This approach ensures consistent image quality and quantification independent of subject motion.
    • The technique offers a computationally efficient and accurate solution for dynamic PET imaging in moving subjects.