Abstract
Fluorescence molecular tomography (FMT) is a promising imaging technique that allows in vivo visualization of molecular-level events associated with disease progression and treatment response. Accurate and efficient 3D reconstruction algorithms will facilitate the wide-use of FMT in preclinical research. Here, we utilize L1/2-norm regularization for improving FMT reconstruction. To efficiently solve the nonconvex L1/2-norm penalized problem, we transform it into a weighted L1-norm minimization problem and employ a homotopy-based iterative reweighting algorithm to recover small fluorescent targets. Both simulations on heterogeneous mouse model and in vivo experiments demonstrated that the proposed L1/2-norm method outperformed the comparative L1-norm reconstruction methods in terms of location accuracy, spatial resolution and quantitation of fluorescent yield. Furthermore, simulation analysis showed the robustness of the proposed method, under different levels of measurement noise and number of excitation sources.
Highlights
As a promising non-invasive molecular imaging technique, in vivo fluorescence molecular tomography (FMT) is an active research topic and exhibits significant potential in many preclinical research, such as monitoring enzyme activity [1], gene expression visualization [2], mapping expressions of cancer markers [3, 4], and monitoring targeted drug delivery [5]
Both simulated data on a 3D digital mouse model and in vivo experimental data acquired by a dual-modality FMT/Micro-CT system were used to validate the potential and feasibility of the proposed iterative reweighted homotopy based L1/2-norm regularization algorithm, which we will call IRW-L1/2
We demonstrated the performance of IRW-L1/2 in FMT with simulated data on a heterogeneous mouse model and in vivo experimental data acquired by a dual-modality FMT/Micro-CT system
Summary
As a promising non-invasive molecular imaging technique, in vivo fluorescence molecular tomography (FMT) is an active research topic and exhibits significant potential in many preclinical research, such as monitoring enzyme activity [1], gene expression visualization [2], mapping expressions of cancer markers [3, 4], and monitoring targeted drug delivery [5]. FMT allows for three-dimensional localization and quantification of fluorescence distribution of targets in small animals to resolve biological processes at molecular and cellular levels [6,7,8,9] This takes place through modeling of near infrared (NIR) light propagation in biological tissues with a forward transportation model, and solving an inverse problem for recovering the distribution of fluorophore concentration from boundary measurements. L2-norm regularization based reconstruction methods are applied to FMT These methods can deal with the ill-posedness of FMT inverse problem, the over-smoothness of L2-norm results in blurred or spread targets with the loss of high-frequency feature in the reconstructed images [14]. The performance of the proposed method in FMT reconstruction was validated with simulated data and in vivo experimental data
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