Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography

• Published in: Physics in medicine & biology Vol. 69; no. 11; pp. 115006 - 115017
• Main Authors: Wu, Yansong, He, Xuelei, Chen, Zihao, Wei, Xiao, Liu, Yanqiu, Li, Shuangchen, Zhang, Heng, Yu, Jingjing, Yi, Huangjian, Guo, Hongbo, He, Xiaowei
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 07.06.2024
• Subjects: Algorithms; Animals; dynamic fluorescence molecular tomography; Fluorescence; group sparsity; Image Processing, Computer-Assisted - methods; Liver - diagnostic imaging; Liver - metabolism; Mice; Optical Imaging - methods; optimization algorithm; Taylor expansion; Tomography - methods; dynamic fluorescence molecular tomography; Taylor expansion; optimization algorithm; group sparsity
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/ad4084

Objective. Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems. Approach. Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem. Main results. Numerical simulation and in vivo experimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs. Significance. Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.