1
|
Cheng X, Sun S, Xiao Y, Li W, Li J, Yu J, Guo H. Fluorescence molecular tomography based on an online maximum a posteriori estimation algorithm. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2024; 41:844-851. [PMID: 38856571 DOI: 10.1364/josaa.519667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 03/25/2024] [Indexed: 06/11/2024]
Abstract
Fluorescence molecular tomography (FMT) is a non-invasive, radiation-free, and highly sensitive optical molecular imaging technique for early tumor detection. However, inadequate measurement information along with significant scattering of near-infrared light within the tissue leads to high ill-posedness in the inverse problem of FMT. To improve the quality and efficiency of FMT reconstruction, we build a reconstruction model based on log-sum regularization and introduce an online maximum a posteriori estimation (OPE) algorithm to solve the non-convex optimization problem. The OPE algorithm approximates a stationary point by evaluating the gradient of the objective function at each iteration, and its notable strength lies in the remarkable speed of convergence. The results of simulations and experiments demonstrate that the OPE algorithm ensures good reconstruction quality and exhibits outstanding performance in terms of reconstruction efficiency.
Collapse
|
2
|
Chu M, Guo H, He X, Wang B, Liu Y, Hu X, Yu J, He X. A Graph-guided Hybrid Regularization Method For Bioluminescence Tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107329. [PMID: 36608432 DOI: 10.1016/j.cmpb.2022.107329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/19/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Bioluminescence tomography (BLT) is a powerful and sensitive imaging technique having great potential in preclinical application, such as tumor imaging, monitoring and therapy, etc. Regularization plays an important role in BLT reconstruction for considering the priori information to overcome the inherent ill-posedness of the inverse problem. Therefore, well-designed regularization term and sophisticated algorithm for solving the consequent optimization problem are key to improve the BLT quality. METHODS To balance the sparsity, smoothness and morphological characteristics of the bioluminescence targets, we constructed a novel Graph-Guided Hybrid Regularization (GGHR) method by combining graph-guided penalty term with L1 and L2 norm regularizer. To solve the corresponding minimization problem with hybrid penalties, the dual decomposition and Nesterov's smoothing technique were adopted to decouple and transform the non-separable and non-smooth graph-guided penalty term into a differential smooth approximation form, which was solved by the fast iterative shrinkage thresholding algorithm. RESULTS The performance of the proposed GGHR method was verified and evaluated through a series of simulation, phantom and in vivo experiments. The comparison results demonstrated that the GGHR method outperformed current mainstream reconstruction algorithms in spatial localization, morphology recovery and in vivo practicality. CONCLUSIONS The proposed GGHR method is a robust and practicality reconstruction algorithm for further highlighting the positive effect of hybrid regularization on BLT applications.
Collapse
Affiliation(s)
- Mengxiang Chu
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Network and Data Center, Northwest University, Xi'an, 710127, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China.
| | - Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China.
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Beilei Wang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Yanqiu Liu
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xiangong Hu
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Network and Data Center, Northwest University, Xi'an, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Xiaowei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Network and Data Center, Northwest University, Xi'an, 710127, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China.
| |
Collapse
|
3
|
Yu J, Dai C, He X, Guo H, Sun S, Liu Y. Bioluminescence Tomography Based on One-Dimensional Convolutional Neural Networks. Front Oncol 2021; 11:760689. [PMID: 34733793 PMCID: PMC8558399 DOI: 10.3389/fonc.2021.760689] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/24/2021] [Indexed: 12/31/2022] Open
Abstract
Bioluminescent tomography (BLT) has increasingly important applications in preclinical studies. However, the simplified photon propagation model and the inherent ill-posedness of the inverse problem limit the quality of BLT reconstruction. In order to improve the reconstruction accuracy of positioning and reconstruction efficiency, this paper presents a deep-learning optical reconstruction method based on one-dimensional convolutional neural networks (1DCNN). The nonlinear mapping relationship between the surface photon flux density and the distribution of the internal bioluminescence sources is directly established, which fundamentally avoids solving the ill-posed inverse problem iteratively. Compared with the previous reconstruction method based on multilayer perceptron, the training parameters in the 1DCNN are greatly reduced and the learning efficiency of the model is improved. Simulations verify the superiority and stability of the 1DCNN method, and the in vivo experimental results further show the potential of the proposed method in practical applications.
Collapse
Affiliation(s)
- Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Chenyang Dai
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Xuelei He
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Hongbo Guo
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Siyu Sun
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Ying Liu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| |
Collapse
|