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

Jae Kyu Choi Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China Chenglong Bao Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China Xiaoqun Zhang Institute of Natural Sciences, School of Mathematical Sciences, and MOE-LSC, Shanghai Jiao Tong University, Shanghai 200240, China

Numerical Analysis and Scientific Computing mathscidoc:2206.25004

SIAM Journal on Imaging Sciences, 11, (2), 1179-1204, 2018.5
Recent technical advances lead to the coupling of PET and MRI scanners, enabling one 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 nonconvex 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 the Kurdyka--Ɓojasiewicz property. Finally, the numerical experiments show that our proposed models achieve better performance over the existing PET-MRI joint reconstruction models.
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@inproceedings{jae2018pet-mri,
  title={PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame Regularization},
  author={Jae Kyu Choi, Chenglong Bao, and Xiaoqun Zhang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220614155252172521360},
  booktitle={SIAM Journal on Imaging Sciences},
  volume={11},
  number={2},
  pages={1179-1204},
  year={2018},
}
Jae Kyu Choi, Chenglong Bao, and Xiaoqun Zhang. PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame Regularization. 2018. Vol. 11. In SIAM Journal on Imaging Sciences. pp.1179-1204. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220614155252172521360.
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