Abstract
Bioluminescence tomography (BLT) is an effective molecular imaging (MI) modality. Because of the ill-posedness, the inverse problem of BLT is still open. We present a trust region method (TRM) for BLT source reconstruction. The TRM is applied in the source reconstruction procedure of BLT for the first time. The results of both numerical simulations and the experiments of cube phantom and nude mouse draw us to the conclusion that based on the adaptive finite element (AFE) framework, the TRM works in the source reconstruction procedure of BLT. To make our conclusion more reliable, we also compare the performance of the TRM and that of the famous Tikhonov regularization method after only one step of mesh refinement of the AFE framework. The conclusion is that the TRM can get faster and better results after only one mesh refinement step of AFE framework than the Tikhonov regularization method when handling large scale data. In the TRM, all the parameters are fixed, while in the Tikhonov method the regularization parameter needs to be well selected.
Highlights
Among many molecular imaging modalities, optical imaging, especially bioluminescence imaging, has attracted remarkable attention for its unique advantages in probing capabilities, sensitivity, specificity, and cost-effectiveness [1, 2, 3] in cancer research [4] and drug development [5]
Based on the adaptive finite element framework, the trust region method has been proposed the first time for Bioluminescence tomography (BLT)
The results could give us the conclusion that trust region method (TRM) can work in solving BLT inverse problem
Summary
Among many molecular imaging modalities, optical imaging, especially bioluminescence imaging, has attracted remarkable attention for its unique advantages in probing capabilities, sensitivity, specificity, and cost-effectiveness [1, 2, 3] in cancer research [4] and drug development [5]. We want to introduce a new kind of method into the reconstruction procedure of BLT problem, the trust region method with all involved parameters fixed. The convergence and regularity of the standard trust region method when applying it to ill-posed problems has been studied by Yanfei Wang and Yaxiang Yuan [25]. After a brief introduction of the AFE framework that the TRM works in for the inverse problem, we will focus on the TRM for the regularization and optimization procedure.
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