PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame RegularizationMS42

Recent technical advances lead to the coupling of PET and MRI scanners, enabling to acquire functional and anatomical data simultaneously. In this talk, 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.

This presentation is part of Minisymposium “MS42 - Low dimensional structures in imaging science (3 parts)
organized by: Wenjing Liao (Georgia Institute of Technology) , Haizhao Yang (Duke University) , Zhizhen Zhao (University of Illinois Urbana-Champaign) .

Jae Kyu Choi (Institute of Natural Sciences, Shanghai Jiao Tong University)
Chenglong Bao (Yau Mathematical Sciences Center, Tsinghua University)
Xiaoqun Zhang (Institute of Natural Sciences, School of Mathematical Sciences, and MOE-LSC, Shanghai Jiao Tong University)
(tight) wavelet frames, data driven tight frames, joint reconstruction, joint sparsity, magnetic resonance imaging, positron emission tomography, proximal alternating scheme