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Wei X, Guo H, Yu J, Liu Y, Zhao Y, He X. Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107711. [PMID: 37451228 DOI: 10.1016/j.cmpb.2023.107711] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 06/24/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Bioluminescence tomography (BLT) is a noninvasive optical imaging technique that provides qualitative and quantitative information on the spatial distribution of tumors in living animals. Researchers have proposed a list of algorithms and strategies for BLT reconstruction to improve its reconstruction quality. However, multi-target BLT reconstruction remains challenging in practical clinical applications due to the mutual interference of optical signals and difficulty in source separation. METHODS To solve this problem, this study proposes the subspace decision optimization (SDO) approach based on the traditional iterative permissible region strategy. The SDO approach transforms a single permissible region into multiple subspaces by clustering analysis. These subspaces are shrunk based on subspace shrinking optimization to achieve spatial continuity of the permissible regions. In addition, these subspaces are merged to construct a new permissible region and then the next iteration of reconstruction is carried out to ensure the stability of the results. Finally, all the iterative results are optimized based on the normal distribution model and the distribution properties of the targets to ensure the sparsity of each target and the non-biasing of the overall results. RESULTS Experimental results show that the SDO approach can automatically identify and separate different targets, ensuring the accuracy and quality of multi-target BLT reconstruction results. Meanwhile, SDO can combine various types of reconstruction algorithms and provide stable and high-quality reconstruction results independent of the algorithm parameters. CONCLUSIONS The SDO approach provides an integrated solution to the multi-target BLT reconstruction problem, realizing the whole process including target recognition, separation, reconstruction, and result enhancement, which can extend the application domain of BLT.
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Affiliation(s)
- Xiao Wei
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China
| | - Hongbo Guo
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China.
| | - Jingjing Yu
- The School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China
| | - Yanqiu Liu
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China
| | - Yingcheng Zhao
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China
| | - Xiaowei He
- The School of Information Sciences and Technology, Northwest University, Xi'an 710069, China; Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an 710127, China.
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Zhang H, Hai L, Kou J, Hou Y, He X, Zhou M, Geng G. OPK_SNCA: Optimized prior knowledge via sparse non-convex approach for cone-beam X-ray luminescence computed tomography imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106645. [PMID: 35091228 DOI: 10.1016/j.cmpb.2022.106645] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 12/24/2021] [Accepted: 01/16/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND The development of Cone-beam X-ray luminescence computed tomography (CB-XLCT) has allowed the quantitative in-depth biological imaging, but with a greatly ill-posed and ill-conditioned inverse problem. Although the predefined permissible source region (PSR) is a widely used way to alleviate the problem for CB-XLCT imaging, how to obtain the accurate PSR is still a challenge for the process of inverse reconstruction. METHODS We proposed an optimized prior knowledge via a sparse non-convex approach (OPK_SNCA) for CB-XLCT imaging. Firstly, non-convex Lp-norm optimization model was employed for copying with the inverse problem, and an iteratively reweighted split augmented lagrangian shrinkage algorithm was developed to obtain a group of sparse solutions based on different non-convex p values. Secondly, a series of permissible regions (PRs) with different discretized mesh was further achieved, and the intersection operation was implemented on the group of PRs to get a reasonable PSR. After that, the final PSR was adopted as an optimized prior knowledge to enhance the reconstruction quality of inverse reconstruction. RESULTS Both simulation experiments and in vivo experiment were performed to evaluate the efficiency and robustness of the proposed method. CONCLUSIONS The experimental results demonstrated that our proposed method could significantly improve the imaging quality of the distribution of X-ray-excitable nanophosphors for CB-XLCT.
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Affiliation(s)
- Haibo Zhang
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China.
| | - Linqi Hai
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Jiaojiao Kou
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Yuqing Hou
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Mingquan Zhou
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Guohua Geng
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
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Wei X, Guo H, Yu J, He X, Yi H, Hou Y, He X. A Multilevel Probabilistic Cerenkov Luminescence Tomography Reconstruction Framework Based on Energy Distribution Density Region Scaling. Front Oncol 2021; 11:751055. [PMID: 34745977 PMCID: PMC8570774 DOI: 10.3389/fonc.2021.751055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Cerenkov luminescence tomography (CLT) is a promising non-invasive optical imaging method with three-dimensional semiquantitative in vivo imaging capability. However, CLT itself relies on Cerenkov radiation, a low-intensity radiation, making CLT reconstruction more challenging than other imaging modalities. In order to solve the ill-posed inverse problem of CLT imaging, some numerical optimization or regularization methods need to be applied. However, in commonly used methods for solving inverse problems, parameter selection significantly influences the results. Therefore, this paper proposed a probabilistic energy distribution density region scaling (P-EDDRS) framework. In this framework, multiple reconstruction iterations are performed, and the Cerenkov source distribution of each reconstruction is treated as random variables. According to the spatial energy distribution density, the new region of interest (ROI) is solved. The size of the region required for the next operation was determined dynamically by combining the intensity characteristics. In addition, each reconstruction source distribution is given a probability weight value, and the prior probability in the subsequent reconstruction is refreshed. Last, all the reconstruction source distributions are weighted with the corresponding probability weights to get the final Cerenkov source distribution. To evaluate the performance of the P-EDDRS framework in CLT, this article performed numerical simulation, in vivo pseudotumor model mouse experiment, and breast cancer mouse experiment. Experimental results show that this reconstruction framework has better positioning accuracy and shape recovery ability and can optimize the reconstruction effect of multiple algorithms on CLT.
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Affiliation(s)
- Xiao Wei
- School of Information and Technology, Northwest University, Xi'an, China.,Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China
| | - Hongbo Guo
- School of Information and Technology, Northwest University, Xi'an, China.,Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Xuelei He
- School of Information and Technology, Northwest University, Xi'an, China.,Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China
| | - Huangjian Yi
- School of Information and Technology, Northwest University, Xi'an, China.,Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China
| | - Yuqing Hou
- School of Information and Technology, Northwest University, Xi'an, China.,Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China
| | - Xiaowei He
- School of Information and Technology, Northwest University, Xi'an, China.,Xi'an Key Laboratory of Radiomics and Intelligent Perception, Northwest University, Xi'an, China
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Hou Y, Tang Z, Yi H, Guo H, Yu J, He X. Three-term conjugate gradient method for X-ray luminescence computed tomography. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2021; 38:985-991. [PMID: 34263754 DOI: 10.1364/josaa.423149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/24/2021] [Indexed: 06/13/2023]
Abstract
X-ray luminescence computed tomography (XLCT) has become an emerging hybrid molecular imaging technology with high detection sensitivity and low cost. However, the inverse problem of reconstruction has severe ill-posed consequences. The original regularization algorithm needs to take much time to solve the problem. To reduce the cost of time, a three-term conjugate gradient (TTCG) algorithm is proposed for XLCT. Useful truncation information is added to the descent direction to find the optimal solution quickly in our proposed algorithm. Both numerical simulation experiments and real experiments are carried out to verify the performance of the algorithm. Experimental results show that the presented algorithm can effectively speed up the reconstruction process.
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Zhang H, Huang X, Zhou M, Geng G, He X. Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography. BIOMEDICAL OPTICS EXPRESS 2020; 11:3717-3732. [PMID: 33014562 PMCID: PMC7510911 DOI: 10.1364/boe.393970] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/19/2020] [Accepted: 06/05/2020] [Indexed: 06/11/2023]
Abstract
Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors in vivo. However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without a prior information is proposed for CB-XLCT. In reconstruction processing, the mesh nodes are automatically selected with higher probability to contribute to the distribution of target for imaging. Specially, an adaptive shrinking function is designed to automatically control the permissible source region at a multi-scale rate. Both 3D digital mouse and in vivo experiments were carried out to test the performance of our method. The results indicate that the proposed framework can dramatically improve the imaging quality of CB-XLCT.
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Affiliation(s)
- Haibo Zhang
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | | | - Mingquan Zhou
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Guohua Geng
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, China
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Chen H, Li T, Fan X, Luo C. Feature selection for imbalanced data based on neighborhood rough sets. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.01.041] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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