Abstract
Driven by recent developments of PET-MRI scanners, simultaneous reconstruction of dual-modality has gained considerable research interest. The purpose of this paper is to propose a joint reconstruction model by utilizing complementary information of dual-modality to improve reconstruction quality of individual images. We propose a total variation based joint regularization with an adaptively estimated common edge indicator function as a weight. The common edge function takes into account the shared structures of two modalities in a flexible way. A proximal alternating algorithm is adopted to recover dual-modality images and the common edge, and the convergence of the overall numerical scheme is established. Finally, numerical tests for PET and MRI under different noise levels and subsampling patterns show that the proposed approach obtains favorable results compared to some existing methods.
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