A robust elastic net-ℓ ₁ ℓ ₂ reconstruction method for x-ray luminescence computed tomography

Abstract

Objective. X-ray luminescence computed tomography (XLCT) has played a crucial role in pre-clinical research and effective diagnosis of disease. However, due to the ill-posed of the XLCT inverse problem, the generalization of reconstruction methods and the selection of appropriate regularization parameters are still challenging in practical applications. In this research, an robust Elastic net-ℓ1ℓ2 reconstruction method is proposed aiming to the challenge. Approach. Firstly, our approach consists of ℓ1 and ℓ2 regularization to enhance the sparsity and suppress the smoothness. Secondly, through optimal approximation of the optimization problem, double modification of Landweber algorithm is adopted to solve the Elastic net-ℓ1ℓ2 regulazation. Thirdly, drawing on the ideal of supervised learning, multi-parameter K-fold cross validation strategy is proposed to determin the optimal parameters adaptively. Main results. To evaluate the performance of the Elastic net-ℓ1ℓ2 method, numerical simulations, phantom and in vivo experiments were conducted. In these experiments, the Elastic net-ℓ1ℓ2 method achieved the minimum reconstruction error (with smallest location error, fluorescent yield relative error, normalized root-mean-square error) and the best image reconstruction quality (with largest contrast-to-noise ratio and Dice similarity) among all methods. The results demonstrated that Elastic net-ℓ1ℓ2 can obtain superior reconstruction performance in terms of location accuracy, dual source resolution, robustness and in vivo practicability. Significance. It is believed that this study will further benefit preclinical applications with a view to provide a more reliable reference for the later researches on XLCT.

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography

“…In the field of DFMT, the distribution characteristics of fluorescence change over time exhibit a certain degree of spatiotemporal continuity and diversity. Additionally, it holds a wealth of structural prior information and internal connections, such as structural sparse regularization prior (Jiang et al 2016, Liu et al 2017, Zhao et al 2021, Guo et al 2022. Hence, the primary challenge in this study is to devise an efficient, suitable and speedy reconstruction method based on these prior knowledge.…”

Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation

“…To enhance the quality of images obtained from a reduced number of projections, which is advantageous for rapid biomedical imaging applications, the utilization of compressed sensing (CS) theory has been employed for reconstruction. Various methods, including the L1-TV method [21], robust elastic net-ℓ1ℓ2 method [22], and T-FISTA method [23], have been utilized to achieve this goal. With the utilization of sparsity or group sparsity as a prior, our research team has successfully shown that CB-XLCT has the capability to differentiate between two targets with an edge-to-edge distance (EED) of 0.1 cm by employing two imaging views [24].…”