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Chen Y, Du M, Li W, Su L, Yi H, Zhao F, Li K, Wang L, Cao X. ABPO-TVSCAD: alternating Bregman proximity operators approach based on TVSCAD regularization for bioluminescence tomography. Phys Med Biol 2022; 67:215013. [PMID: 36220011 DOI: 10.1088/1361-6560/ac994c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Objective.Bioluminescence tomography (BLT) is a promising non-invasive optical medical imaging technique, which can visualize and quantitatively analyze the distribution of tumor cells in living tissues. However, due to the influence of photon scattering effect and ill-conditioned inverse problem, the reconstruction result is unsatisfactory. The purpose of this study is to improve the reconstruction performance of BLT.Approach.An alternating Bregman proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT reconstruction. TVSCAD combines the anisotropic total variation (TV) regularization constraints and the non-convex smoothly clipped absolute deviation (SCAD) penalty constraints, to make a trade-off between the sparsity and edge preservation of the source. ABPO approach is used to solve the TVSCAD model (ABPO-TVSCAD for short). In addition, to accelerate the convergence speed of the ABPO, we adapt the strategy of shrinking the permission source region, which further improves the performance of ABPO-TVSCAD.Main results.The results of numerical simulations andin vivoxenograft mouse experiment show that our proposed method achieved superior accuracy in spatial localization and morphological reconstruction of bioluminescent source.Significance.ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the development of optical molecular tomography.
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Affiliation(s)
- Yi Chen
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Mengfei Du
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Weitong Li
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Linzhi Su
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Huangjian Yi
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Fengjun Zhao
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Kang Li
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
| | - Lin Wang
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, People's Republic of China
| | - Xin Cao
- School of Information Sciences and Technology, Northwest University, Xi'an 710127, People's Republic of China
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Liu Y, Chu M, Guo H, Hu X, Yu J, He X, Yi H, He X. Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography. Front Oncol 2022; 12:768137. [PMID: 35251958 PMCID: PMC8895370 DOI: 10.3389/fonc.2022.768137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Bioluminescence tomography (BLT) is a promising in vivo molecular imaging tool that allows non-invasive monitoring of physiological and pathological processes at the cellular and molecular levels. However, the accuracy of the BLT reconstruction is significantly affected by the forward modeling errors in the simplified photon propagation model, the measurement noise in data acquisition, and the inherent ill-posedness of the inverse problem. In this paper, we present a new multispectral differential strategy (MDS) on the basis of analyzing the errors generated from the simplification from radiative transfer equation (RTE) to diffusion approximation and data acquisition of the imaging system. Through rigorous theoretical analysis, we learn that spectral differential not only can eliminate the errors caused by the approximation of RTE and imaging system measurement noise but also can further increase the constraint condition and decrease the condition number of system matrix for reconstruction compared with traditional multispectral (TM) reconstruction strategy. In forward simulations, energy differences and cosine similarity of the measured surface light energy calculated by Monte Carlo (MC) and diffusion equation (DE) showed that MDS can reduce the systematic errors in the process of light transmission. In addition, in inverse simulations and in vivo experiments, the results demonstrated that MDS was able to alleviate the ill-posedness of the inverse problem of BLT. Thus, the MDS method had superior location accuracy, morphology recovery capability, and image contrast capability in the source reconstruction as compared with the TM method and spectral derivative (SD) method. In vivo experiments verified the practicability and effectiveness of the proposed method.
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Affiliation(s)
- 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, China
| | - Mengxiang Chu
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- Network and Data Center, Northwest University, Xi’an, 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, China
- *Correspondence: Hongbo Guo, ; Xiaowei He,
| | - Xiangong Hu
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- Network and Data Center, Northwest University, Xi’an, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, 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, China
| | - Huangjian Yi
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Xiaowei He
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
- Network and Data Center, Northwest University, Xi’an, China
- *Correspondence: Hongbo Guo, ; Xiaowei He,
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Yin L, Cao Z, Wang K, Tian J, Yang X, Zhang J. A review of the application of machine learning in molecular imaging. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:825. [PMID: 34268438 PMCID: PMC8246214 DOI: 10.21037/atm-20-5877] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/02/2020] [Indexed: 12/12/2022]
Abstract
Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI. OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural network-based machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.
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Affiliation(s)
- Lin Yin
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Zhen Cao
- Peking University First Hospital, Beijing, China
| | - Kun Wang
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Xing Yang
- Peking University First Hospital, Beijing, China
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Cao X, Li K, Xu XL, Deneen KMV, Geng GH, Chen XL. Development of tomographic reconstruction for three-dimensional optical imaging: From the inversion of light propagation to artificial intelligence. Artif Intell Med Imaging 2020; 1:78-86. [DOI: 10.35711/aimi.v1.i2.78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/01/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
Optical molecular tomography (OMT) is an imaging modality which uses an optical signal, especially near-infrared light, to reconstruct the three-dimensional information of the light source in biological tissue. With the advantages of being low-cost, noninvasive and having high sensitivity, OMT has been applied in preclinical and clinical research. However, due to its serious ill-posedness and ill-condition, the solution of OMT requires heavy data analysis and the reconstruction quality is limited. Recently, the artificial intelligence (commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem. In this paper, we review the progress on OMT algorithms, from conventional methods to AI-based methods, and we also give a prospective towards future developments in this domain.
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Affiliation(s)
- Xin Cao
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Kang Li
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Xu
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Karen M von Deneen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
| | - Guo-Hua Geng
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
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An Y, Meng H, Gao Y, Tong T, Zhang C, Wang K, Tian J. Application of machine learning method in optical molecular imaging: a review. SCIENCE CHINA INFORMATION SCIENCES 2020; 63:111101. [DOI: 10.1007/s11432-019-2708-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 09/17/2019] [Accepted: 10/22/2019] [Indexed: 08/30/2023]
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6
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Yin L, Wang K, Tong T, An Y, Meng H, Yang X, Tian J. Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography. IEEE Trans Biomed Eng 2019; 67:2023-2032. [PMID: 31751214 DOI: 10.1109/tbme.2019.2953732] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Bioluminescence tomography (BLT) is a non-invasive technique designed to enable three-dimensional (3D) visualization and quantification of viable tumor cells in living organisms. However, despite the excellent sensitivity and specificity of bioluminescence imaging (BLI), BLT is limited by the photon scattering effect and ill-posed inverse problem. If the complete structural information of a light source is considered when solving the inverse problem, reconstruction accuracy will be improved. METHODS This article proposed a block sparse Bayesian learning method based on K-nearest neighbor strategy (KNN-BSBL), which incorporated several types of a priori information including sparsity, spatial correlations among neighboring points, and anatomical information to balance over-sparsity and morphology preservation in BLT. Furthermore, we considered the Gaussian weighted distance prior in a light source and proposed a KNN-GBSBL method to further improve the performance of KNN-BSBL. RESULTS The results of numerical simulations and in vivo glioma-bearing mouse experiments demonstrated that KNN-BSBL and KNN-GBSBL achieved superior accuracy for tumor spatial positioning and morphology reconstruction. CONCLUSION The proposed method KNN-BSBL incorporated several types of a priori information is an efficient and robust reconstruction method for BLT.
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Yang D, Wang L, Chen D, Yan C, He X, Liang J, Chen X. Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography. Med Biol Eng Comput 2018; 56:2067-2081. [PMID: 29770920 DOI: 10.1007/s11517-018-1842-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 05/04/2018] [Indexed: 12/17/2022]
Abstract
The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment. Graphical abstract The filtered MLEM-based global reconstruction method for BLT.
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Affiliation(s)
- Defu Yang
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lin Wang
- School of Information Sciences and Technology, Northwest University, Xi'an, 710126, China
| | - Dongmei Chen
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Chenggang Yan
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, 710126, China
| | - Jimin Liang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710127, China
| | - Xueli Chen
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710127, China.
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Guo H, Yu J, Hu Z, Yi H, Hou Y, He X. A hybrid clustering algorithm for multiple-source resolving in bioluminescence tomography. JOURNAL OF BIOPHOTONICS 2018; 11:e201700056. [PMID: 28700135 DOI: 10.1002/jbio.201700056] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 06/20/2017] [Accepted: 06/21/2017] [Indexed: 05/23/2023]
Abstract
Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and K-means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple-source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1-luc2 mouse model were conducted to assess the performance of the proposed method in multiple-source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications.
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Affiliation(s)
- Hongbo Guo
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
- Chinese Academy of Sciences, Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Jingjing Yu
- The School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Zhenhua Hu
- Chinese Academy of Sciences, Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Huangjian Yi
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Yuqing Hou
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Xiaowei He
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
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Gao Y, Wang K, Jiang S, Liu Y, Ai T, Tian J. Bioluminescence Tomography Based on Gaussian Weighted Laplace Prior Regularization for In Vivo Morphological Imaging of Glioma. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2343-2354. [PMID: 28796614 DOI: 10.1109/tmi.2017.2737661] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Bioluminescence tomography (BLT) is a powerful non-invasive molecular imaging tool for in vivo studies of glioma in mice. However, because of the light scattering and resulted ill-posed problems, it is challenging to develop a sufficient reconstruction method, which can accurately locate the tumor and define the tumor morphology in three-dimension. In this paper, we proposed a novel Gaussian weighted Laplace prior (GWLP) regularization method. It considered the variance of the bioluminescence energy between any two voxels inside an organ had a non-linear inverse relationship with their Gaussian distance to solve the over-smoothed tumor morphology in BLT reconstruction. We compared the GWLP with conventional Tikhonov and Laplace regularization methods through various numerical simulations and in vivo orthotopic glioma mouse model experiments. The in vivo magnetic resonance imaging and ex vivo green fluorescent protein images and hematoxylin-eosin stained images of whole head cryoslicing specimens were utilized as gold standards. The results demonstrated that GWLP achieved the highest accuracy in tumor localization and tumor morphology preservation. To the best of our knowledge, this is the first study that achieved such accurate BLT morphological reconstruction of orthotopic glioma without using any segmented tumor structure from any other structural imaging modalities as the prior for reconstruction guidance. This enabled BLT more suitable and practical for in vivo imaging of orthotopic glioma mouse models.
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He X, Dong F, Yu J, Guo H, Hou Y. Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2015; 32:1928-1935. [PMID: 26560906 DOI: 10.1364/josaa.32.001928] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Fluorescence molecular tomography (FMT) has been a promising imaging tool that provides convenience for accurate localization and quantitative analysis of the fluorescent probe. In this study, we present a reconstruction method combining sorted L-one penalized estimation with an iterative-shrinking permissible region strategy to reconstruct fluorescence targets. Both numerical simulation experiments on a three-dimensional digital mouse model and physical experiments on a cubic phantom were carried out to validate the accuracy, effectiveness, and robustness of the proposed method. The results indicate that the proposed method can produce better location and satisfactory fluorescent yield with computational efficiency, which makes it a practical and promising reconstruction method for FMT.
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11
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Ping Wu, Yifang Hu, Kun Wang, Jie Tian. Bioluminescence Tomography by an Iterative Reweighted ${\bm {l_{2}}}$-Norm Optimization. IEEE Trans Biomed Eng 2014; 61:189-96. [DOI: 10.1109/tbme.2013.2279190] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Lewis MA, Richer E, Slavine NV, Kodibagkar VD, Soesbe TC, Antich PP, Mason RP. A Multi-Camera System for Bioluminescence Tomography in Preclinical Oncology Research. Diagnostics (Basel) 2013; 3:325-43. [PMID: 26824926 PMCID: PMC4665465 DOI: 10.3390/diagnostics3030325] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2013] [Revised: 06/13/2013] [Accepted: 06/26/2013] [Indexed: 01/11/2023] Open
Abstract
Bioluminescent imaging (BLI) of cells expressing luciferase is a valuable noninvasive technique for investigating molecular events and tumor dynamics in the living animal. Current usage is often limited to planar imaging, but tomographic imaging can enhance the usefulness of this technique in quantitative biomedical studies by allowing accurate determination of tumor size and attribution of the emitted light to a specific organ or tissue. Bioluminescence tomography based on a single camera with source rotation or mirrors to provide additional views has previously been reported. We report here in vivo studies using a novel approach with multiple rotating cameras that, when combined with image reconstruction software, provides the desired representation of point source metastases and other small lesions. Comparison with MRI validated the ability to detect lung tumor colonization in mouse lung.
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Affiliation(s)
- Matthew A Lewis
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
| | - Edmond Richer
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
- Department of Mechanical Engineering, Southern Methodist University, Dallas, TX 75275, USA.
| | - Nikolai V Slavine
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
| | - Vikram D Kodibagkar
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA.
| | - Todd C Soesbe
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
- Advanced Imaging Research Center, UT Southwestern, Dallas, TX 75390, USA.
| | - Peter P Antich
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
| | - Ralph P Mason
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390, USA.
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Yu J, He X, Geng G, Liu F, Jiao LC. Hybrid multilevel sparse reconstruction for a whole domain bioluminescence tomography using adaptive finite element. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:548491. [PMID: 23533542 PMCID: PMC3603587 DOI: 10.1155/2013/548491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2012] [Accepted: 01/26/2013] [Indexed: 11/18/2022]
Abstract
Quantitative reconstruction of bioluminescent sources from boundary measurements is a challenging ill-posed inverse problem owing to the high degree of absorption and scattering of light through tissue. We present a hybrid multilevel reconstruction scheme by combining the ability of sparse regularization with the advantage of adaptive finite element method. In view of the characteristics of different discretization levels, two different inversion algorithms are employed on the initial coarse mesh and the succeeding ones to strike a balance between stability and efficiency. Numerical experiment results with a digital mouse model demonstrate that the proposed scheme can accurately localize and quantify source distribution while maintaining reconstruction stability and computational economy. The effectiveness of this hybrid reconstruction scheme is further confirmed with in vivo experiments.
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Affiliation(s)
- Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, Shanxi 710062, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, Shanxi 710069, China
| | - Guohua Geng
- School of Information Sciences and Technology, Northwest University, Xi'an, Shanxi 710069, China
| | - Fang Liu
- School of Computer Science and Technology, Xidian University, Xi'an, Shanxi 710071, China
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi'an, Shanxi 710071, China
| | - L. C. Jiao
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xi'an, Shanxi 710071, China
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Chen D, Zhu S, Yi H, Zhang X, Chen D, Liang J, Tian J. Cone beam x-ray luminescence computed tomography: A feasibility study. Med Phys 2013; 40:031111. [DOI: 10.1118/1.4790694] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Three-dimensional noninvasive monitoring iodine-131 uptake in the thyroid using a modified Cerenkov luminescence tomography approach. PLoS One 2012; 7:e37623. [PMID: 22629431 PMCID: PMC3358266 DOI: 10.1371/journal.pone.0037623] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2011] [Accepted: 04/23/2012] [Indexed: 01/09/2023] Open
Abstract
Background Cerenkov luminescence tomography (CLT) provides the three-dimensional (3D) radiopharmaceutical biodistribution in small living animals, which is vital to biomedical imaging. However, existing single-spectral and multispectral methods are not very efficient and effective at reconstructing the distribution of the radionuclide tracer. In this paper, we present a semi-quantitative Cerenkov radiation spectral characteristic-based source reconstruction method named the hybrid spectral CLT, to efficiently reconstruct the radionuclide tracer with both encouraging reconstruction results and less acquisition and image reconstruction time. Methodology/Principal Findings We constructed the implantation mouse model implanted with a 400 µCi Na131I radioactive source and the physiological mouse model received an intravenous tail injection of 400 µCi radiopharmaceutical Iodine-131 (I-131) to validate the performance of the hybrid spectral CLT and compared the reconstruction results, acquisition, and image reconstruction time with that of single-spectral and multispectral CLT. Furthermore, we performed 3D noninvasive monitoring of I-131 uptake in the thyroid and quantified I-131 uptake in vivo using hybrid spectral CLT. Results showed that the reconstruction based on the hybrid spectral CLT was more accurate in localization and quantification than using single-spectral CLT, and was more efficient in the in vivo experiment compared with multispectral CLT. Additionally, 3D visualization of longitudinal observations suggested that the reconstructed energy of I-131 uptake in the thyroid increased with acquisition time and there was a robust correlation between the reconstructed energy versus the gamma ray counts of I-131 (). The ex vivo biodistribution experiment further confirmed the I-131 uptake in the thyroid for hybrid spectral CLT. Conclusions/Significance Results indicated that hybrid spectral CLT could be potentially used for thyroid imaging to evaluate its function and monitor its treatment for thyroid cancer.
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