Recent technical advances lead to the coupling of PET and MRI scanners, enabling to acquire functional and anatomical data simultaneously. In this paper, we propose a tight frame based PET-MRI joint reconstruction model via the joint sparsity of tight frame coefficients. In addition, a non-convex balanced approach is adopted to take the different regularities of PET and MRI images into account. To solve the nonconvex and nonsmooth model, a proximal alternating minimization algorithm is proposed, and the global convergence is present based on Kurdyka-Lojasiewicz property. Finally, the numerical experiments show that the our proposed models achieve better performance over the existing PET-MRI joint reconstruction models.
@article{arxiv.1705.08654,
title = {PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame Regularization},
author = {Jae Kyu Choi and Chenglong Bao and Xiaoqun Zhang},
journal= {arXiv preprint arXiv:1705.08654},
year = {2018}
}