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Image reconstruction for positron emission tomography (PET) is challengingbecause of the ill-conditioned tomographic problem and low counting statistics.Kernel methods address this challenge by using kernel representation toincorporate image prior information in the forward model of iterative PET imagereconstruction. Existing kernel methods construct the kernels commonly using anempirical process, which may lead to unsatisfactory performance. In this paper,we describe the equivalence between the kernel representation and a trainableneural network model. A deep kernel method is then proposed by exploiting adeep neural network to enable automated learning of an improved kernel modeland is directly applicable to single subjects in dynamic PET. The trainingprocess utilizes available image prior data to form a set of robust kernels inan optimized way rather than empirically. The results from computer simulationsand a real patient dataset demonstrate that the proposed deep kernel method canoutperform the existing kernel method and neural network method for dynamic PETimage reconstruction.

Deep Kernel Representation for Image Reconstruction in PET