Statistical Image Reconstruction for Shortened Dynamic PET Using a Dual Kernel Method

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Dynamic F-18 FDG PET imaging along with tracer kinetic modeling can provide parametric images of physiologically important parameters for characterization of tumor and other diseases. This technique often requires a 1-hour long scanning time, which is less practical in clinic; a more practical method is to use a shortened dynamic scan time of thirty or forty minutes. However, a shortened dynamic scan acquires less data and tracer kinetic modeling becomes more sensitive to high noise in dynamic PET. To address the noise challenge, the kernel method has been developed for efficient dynamic PET image reconstruction. Previous kernel approaches use a single kernel type, which exploits either nonlocal or local spatial correlations from image priors but does not explore the full potential of the kernel framework. In this work, we propose a new dualkernel approach to further enhance kernel-based dynamic PET image reconstruction. The dual kernel combines the existing non-local kernel with a local convolutional kernel that can be easily trained from image priors. We evaluated the new kernel approach for shortened dynamic FDG-PET imaging using a digital brain phantom. Simulation results have demonstrated that the dual-kernel approach can achieve better image quality than standard reconstruction approach and the single kernel approach.