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Positron emission tomography (PET) is widely used for clinical diagnosis. AsPET suffers from low resolution and high noise, numerous efforts try toincorporate anatomical priors into PET image reconstruction, especially withthe development of hybrid PET/CT and PET/MRI systems. In this work, we proposeda novel 3D structural convolutional sparse coding (CSC) concept forpenalized-likelihood PET image reconstruction, named 3D PET-CSC. The proposed3D PET-CSC takes advantage of the convolutional operation and manages toincorporate anatomical priors without the need of registration or supervisedtraining. As 3D PET-CSC codes the whole 3D PET image, instead of patches, italleviates the staircase artifacts commonly presented in traditionalpatch-based sparse coding methods. Moreover, we developed the residual-imageand order-subset mechanisms to further reduce the computational cost andaccelerate the convergence for the proposed 3D PET-CSC method. Experimentsbased on computer simulations and clinical datasets demonstrate the superiorityof 3D PET-CSC compared with other reference methods.

Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding