Reconstruction method for fluorescence molecular tomography based on L1-norm primal accelerated proximal gradient

Abstract

Fluorescence molecular tomography (FMT) has been widely used in preclinical tumor imaging, which enables three-dimensional imaging of the distribution of fluorescent probes in small animal bodies via image reconstruction method. However, the reconstruction results are usually unsatisfactory in the term of robustness and efficiency because of the ill-posed and ill-conditioned of FMT problem. In this study, an FMT reconstruction method based on primal accelerated proximal gradient (PAPG) descent and L1-norm regularized projection (L1RP) is proposed. The proposed method utilizes the current and previous iterations to obtain a search point at each iteration. To achieve fast convergence, the PAPG method is applied to efficiently solve the search point, and then L1RP is performed to obtain the robust and accurate reconstruction. To verify the performance of the proposed method, simulation experiments are conducted. The comparative results revealed that it held advantages of robustness, accuracy, and efficiency in FMT reconstructions. Furthermore, a phantom experiment and an in vivo mouse experiment were also performed, which proved the potential and feasibility of the proposed method for practical applications.

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

“…Photon propagation in the near-infrared spectral band has a strongly scattering characteristic in the biological tissues. For steady-state FMT reconstruction with a point excitation source, the diffusion equation (DE) with the Robin-type boundary condition can be used to describe the propagation process of photons in the biological tissues (Lee et al 2007, Liu et al 2018. This can be expressed as follows:…”

Performance enhancement of diffuse fluorescence tomography based on an extended Kalman filtering-long short term memory neural network correction model

“…The most commonly used strategy is known as regularization including Tikhonov regularization (L2 norm), Lp (0 < p ≤ 1) norm and total variation applied independently or jointly. Tikhonov regularization [ 6 , 7 ] is one of the most popular approaches to solve the inverse problem of DFT, in which a L2-norm constraint term is added into the data-fitting term to improve the stability, while it tends to cause artifacts and over-smoothed image boundary. As a sparsity constraint, L1 regularization [ 8 , 9 ] using prior knowledge of the sparse distribution of fluorescent sources is another effective strategy.…”

A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography

“…In recent years, research on FMT has mainly been focused on improving the 3-D reconstruction quality, especially the location accuracy and morphological recovery ability [7,8]. Generally, FMT reconstruction requires the establishment of a correspondence between the known surface fluorescence distribution of the imaged object and the unknown distribution of the fluorescent probes inside it; then, the unknown information is obtained using a specific solution method.…”