An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

Author:

Li Jintao12,Zhang Lizhi12,Liu Jia3,Zhang Diya12,Kang Dizhen12,Wang Beilei12,He Xiaowei12ORCID,Zhang Heng12,Zhao Yizhe12,Guo Hongbo12ORCID,Hou Yuqing12

Affiliation:

1. The Xi'an Key Laboratory of Radiomics and Intelligent Perception Xi'an China

2. School of Information Sciences and Technology Northwest University Xi'an China

3. Xi'an Company of Shaanxi Tobacco Company, The Information Center Xi'an China

Abstract

AbstractTo alleviate the ill‐posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry

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