Reconstruction of Pet Image Based on Kernelized Expectation-maximization Method

Positron emission tomography (PET) image reconstruction is challenging for low count frame. The reconstruction is important method to retrieve information that has been lost in the images. To improve image quality, prior information are used. Based on kernel method, PET image intensity in each pixel is obtained from prior information and the coefficients can be estimated by the maximum likelihood (ML).This paper proposes a kernelized expectation maximization (EM) algorithm to obtain the ML estimate. It has simplicity as ML EM reconstruction. PET image is constructed as kernel matrix. In dynamic PET image reconstruction, proper numbers of composite frames is important for reconstructing high quality images and reduce noise. Comparing with other methods, existing method is done by iterations, but the proposed method is done by matrix form and it provides better image quality. This experimental result shows improved reconstructed image and also reduce noise.