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Neural KEM: A Kernel Method with Deep Coefficient Prior for PET Image Reconstruction
In this paper, we propose an implicit regularization for the kernel method by using a deep coefficient prior, which represents the kernel coefficient image in the PET forward model using a convolutional neural-network.
Deep Kernel Representation for Image Reconstruction in PET
Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction.
Low-Dose CT Image Denoising Using Parallel-Clone Networks
In this paper, we propose a parallel-clone neural network method that utilizes a modularized network model and exploits the benefit of parallel input, parallel-output loss, and clone-toclone feature transfer.
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