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 (GSTE) 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 (AGD) 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.
Keywords: Taylor expansion; dynamic fluorescence molecular tomography; group sparsity; optimization algorithm.
© 2024 Institute of Physics and Engineering in Medicine.