Novel Regularized Sparse Model for Fluorescence Molecular Tomography Reconstruction

Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in small animals. FMT has been used in surgical navigation for tumor resection and has many potential applications at the physiological, metabolic, and molecular levels in tissues. The hybrid system combined FMT and X-ray computed tomography (XCT) was pursued for accurate detection. However, the result is usually over-smoothed and over-shrunk. In this paper, we propose a region reconstruction method for FMT in which the elastic net (E-net) regularization is used to combine L1-norm and L2-norm. The E-net penalty corresponds to adding the L1-norm penalty and a L2-norm penalty. Elastic net combines the advantages of L1-norm regularization and L2-norm regularization. It could achieve the balance between the sparsity and smooth by simultaneously employing the L1-norm and the L2-norm. To solve the problem effectively, the proximal gradient algorithms was used to accelerate the computation. To evaluate the performance of the proposed E-net method, numerical phantom experiments are conducted. The simulation study shows that the proposed method achieves accurate and is able to reconstruct image effectively.