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Liu T, Ruan J, Rong J, Hao W, Li W, Li R, Zhan Y, Lu H. Cone-beam X-ray luminescence computed tomography based on MLEM with adaptive FISTA initial image. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107265. [PMID: 36455470 DOI: 10.1016/j.cmpb.2022.107265] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/07/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE As an emerging dual-mode optical molecular imaging, cone-beam X-ray luminescence computed tomography (CB-XLCT) has shown potential in early tumor diagnosis and other applications with increased depth and little autofluorescence. However, due to the low transfer efficiency of PNPs to convert X-ray energy to visible or near-infrared (NIR) light and X-ray dose limitation, the signal to noise ratio of projections is quite low, making the quality of CB-XLCT relatively poor. METHODS To improve the reconstruction quality of low-counts CB-XLCT imaging, an adaptive reconstruction algorithm (named ADFISTA-MLEM) based on the maximum likelihood expectation estimation (MLEM) framework is proposed for CB-XLCT reconstruction from Poisson distributed projections. In the proposed framework, the image reconstructed by fast iterative shrinkage-thresholding algorithm (FISTA) is used as the initial image for MLEM iterations to improve reconstruction accuracy, in which both the projection noise model and the sparsity constraint of the image could be considered. For relative quantitative imaging, a specific normalization is applied to the projection data and system matrix. To determine the hyperparameter of FISTA, which may be different for different projections, an adaptive strategy (ADFISTA) is then designed for adaptive update of the hyperparameter with reconstructed image in each iteration. RESULTS AND CONCLUSIONS Results from numerical simulations and phantom experiments indicate that the proposed framework can obtain superior reconstruction accuracy in terms of contrast to noise ratio and shape similarity. In addition, high intensity-concentration linearity between different probe targets indicates its potential for quantitative CB-XLCT imaging.
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Affiliation(s)
- Tianshuai Liu
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Jiabin Ruan
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Junyan Rong
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China.
| | - Wenqing Hao
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Wangyang Li
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Ruijing Li
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China
| | - Yonghua Zhan
- School of Life Science & Technology, Xidian University, Xi'an, China.
| | - Hongbing Lu
- Biomedical Engineering Department, Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China.
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Feng J, Jia K, Li Z, Pogue BW, Yang M, Wang Y. Bayesian sparse-based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational time. JOURNAL OF BIOPHOTONICS 2018; 11:e201700214. [PMID: 29119702 DOI: 10.1002/jbio.201700214] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 11/07/2017] [Indexed: 06/07/2023]
Abstract
Bioluminescence tomography (BLT) provides fundamental insight into biological processes in vivo. To fully realize its potential, it is important to develop image reconstruction algorithms that accurately visualize and quantify the bioluminescence signals taking advantage of limited boundary measurements. In this study, a new 2-step reconstruction method for BLT is developed by taking advantage of the sparse a priori information of the light emission using multispectral measurements. The first step infers a wavelength-dependent prior by using all multi-wavelength measurements. The second step reconstructs the source distribution based on this developed prior. Simulation, phantom and in vivo results were performed to assess and compare the accuracy and the computational efficiency of this algorithm with conventional sparsity-promoting BLT reconstruction algorithms, and results indicate that the position errors are reduced from a few millimeters down to submillimeter, and reconstruction time is reduced by 3 orders of magnitude in most cases, to just under a few seconds. The recovery of single objects and multiple (2 and 3) small objects is simulated, and the recovery of images of a mouse phantom and an experimental animal with an existing luminescent source in the abdomen is demonstrated. Matlab code is available at https://github.com/jinchaofeng/code/tree/master.
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Affiliation(s)
- Jinchao Feng
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124
| | - Kebin Jia
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124
| | - Zhe Li
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
| | - Brian W Pogue
- Thayer school of Engineering, Dartmouth College, Hanover, New Hampshire
| | - Mingjie Yang
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
| | - Yaqi Wang
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
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Ren S, Hu H, Li G, Cao X, Zhu S, Chen X, Liang J. Multi-atlas registration and adaptive hexahedral voxel discretization for fast bioluminescence tomography. BIOMEDICAL OPTICS EXPRESS 2016; 7:1549-60. [PMID: 27446674 PMCID: PMC4929660 DOI: 10.1364/boe.7.001549] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 03/14/2016] [Accepted: 03/23/2016] [Indexed: 05/25/2023]
Abstract
Bioluminescence tomography (BLT) has been a valuable optical molecular imaging technique to non-invasively depict the cellular and molecular processes in living animals with high sensitivity and specificity. Due to the inherent ill-posedness of BLT, a priori information of anatomical structure is usually incorporated into the reconstruction. The structural information is usually provided by computed tomography (CT) or magnetic resonance imaging (MRI). In order to obtain better quantitative results, BLT reconstruction with heterogeneous tissues needs to segment the internal organs and discretize them into meshes with the finite element method (FEM). It is time-consuming and difficult to handle the segmentation and discretization problems. In this paper, we present a fast reconstruction method for BLT based on multi-atlas registration and adaptive voxel discretization to relieve the complicated data processing procedure involved in the hybrid BLT/CT system. A multi-atlas registration method is first adopted to estimate the internal organ distribution of the imaged animal. Then, the animal volume is adaptively discretized into hexahedral voxels, which are fed into FEM for the following BLT reconstruction. The proposed method is validated in both numerical simulation and an in vivo study. The results demonstrate that the proposed method can reconstruct the bioluminescence source efficiently with satisfactory accuracy.
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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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Yu Q, Fan W, Cao F. Mechanistic molecular imaging of cardiac cell therapy for ischemic heart disease. Am J Physiol Heart Circ Physiol 2013; 305:H947-59. [PMID: 23893164 DOI: 10.1152/ajpheart.00092.2013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Cell-based myocardial regeneration has emerged as a promising therapeutic option for ischemic heart disease, though not yet at the level of routine clinical utility. Despite the encouraging results from initial preclinical studies that have demonstrated improved function and reduced infarct size of the ischemic myocardium following several candidate cell transplantation, the beneficial effects and molecular mechanisms of cardiac cell therapy are still unclear in clinical applications to date, and much remains to be optimized. To improve engraftment, accurate methods are required for tracking cell fate and quantifying functional outcome. In the present review, we summarized the current status and challenges of cardiac cell therapy for ischemic heart disease and discussed the strengths and limitations of currently available in vivo imaging techniques with special focus on the newly developed multimodality approaches for assessing the efficacy of engrafted donor cells. We also addressed the hurdles these imaging modalities are facing, including issues regarding immunogenicity and tumorigenicity of transplanted stem cells, and provided some the future perspectives on stem cell imaging.
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Affiliation(s)
- Qiujun Yu
- Department of Cardiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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Hu Z, Yang W, Ma X, Ma W, Qu X, Liang J, Wang J, Tian J. Cerenkov Luminescence Tomography of Aminopeptidase N (APN/CD13) Expression in Mice Bearing HT1080 Tumors. Mol Imaging 2013; 12:7290.2012.00030. [DOI: 10.2310/7290.2012.00030] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023] Open
Affiliation(s)
- Zhenhua Hu
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weidong Yang
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xiaowei Ma
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wenhui Ma
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xiaochao Qu
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jimin Liang
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jing Wang
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- From the School of Life Sciences and Technology, Xidian University, Xi'an, China; Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China; and Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Fan W, Sun D, Liu J, Liang D, Wang Y, Narsinh KH, Li Y, Qin X, Liang J, Tian J, Cao F. Adipose stromal cells amplify angiogenic signaling via the VEGF/mTOR/Akt pathway in a murine hindlimb ischemia model: a 3D multimodality imaging study. PLoS One 2012; 7:e45621. [PMID: 23029141 PMCID: PMC3447795 DOI: 10.1371/journal.pone.0045621] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 08/20/2012] [Indexed: 01/24/2023] Open
Abstract
Although adipose-derived stromal cell (ADSC) transplantation has been demonstrated as a promising therapeutic strategy for peripheral arterial disease (PAD), the mechanism of action behind the observed therapeutic efficacy of ADSCs remains unclear. This study was designed to investigate the long-term outcome and therapeutic behavior of engrafted ADSCs in a murine hindlimb ischemia model using multimodality molecular imaging approaches. ADSCs (1.0×107) were isolated from Tg(Fluc-egfp) mice which constitutively express dual-reporter firefly luciferase and enhanced green fluorescent protein (Fluc+-eGFP+, mADSCsFluc+GFP+), then intramuscularly injected into the hindlimb of BALB/c-nu mice after unilateral femoral artery ligation and excision. Abbreviated survival (∼5 weeks) of post-transplant mADSCs within the ischemic hindlimb was longitudinally monitored using noninvasive bioluminescence imaging (BLI), fluorescence imaging (FRI), and bioluminescence tomography with micro-computed tomography (BLT/micro-CT). Use of the BLT/micro-CT system enabled quantitative 3-dimensional (3D) imaging of the cells’ distribution and kinetics in vivo. Engrafted mADSCs improved blood perfusion recovery, ambulatory performance and prognosis of the ischemic hindlimb, probably by inducing angiogenesis and formation of collateral vessels, which could be visualized using laser Doppler perfusion imaging (LDPI), micro-CT angiography, vascular-cast imaging, and immunofluorescence. mADSCs augmented activation of the pro-angiogenic VEGF/mTOR/Akt pathway in vivo, even though the cells failed to incorporate into the host microvasculature as functional components. Downregulation of VEGF/mTOR/Akt signaling using small molecule inhibitors counteracted mADSC-induced angiogenesis and perfusion restoration. This study demonstrates for the first time the spatiotemporal kinetics and functional survival of transplanted mADSCs in a PAD model using in vivo 3D multimodality imaging. Our study indicates that mADSCs potentiate pro-angiogenic signal amplification via a VEGF/mTOR/Akt-dependent pathway, and thereby promote recovery from hindlimb ischemia.
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Affiliation(s)
- Weiwei Fan
- Department of Cardiology & Molecular Imaging Program, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Dongdong Sun
- Department of Cardiology & Molecular Imaging Program, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Junting Liu
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi’an, Shaanxi, China
| | - Dong Liang
- Department of Cardiology & Molecular Imaging Program, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Yabin Wang
- Department of Cardiology & Molecular Imaging Program, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Kazim H. Narsinh
- Department of Radiology & Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Yong Li
- Department of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Xing Qin
- Department of Cardiology & Molecular Imaging Program, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
| | - Jimin Liang
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi’an, Shaanxi, China
| | - Jie Tian
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi’an, Shaanxi, China
- * E-mail: (FC); (JT)
| | - Feng Cao
- Department of Cardiology & Molecular Imaging Program, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China
- * E-mail: (FC); (JT)
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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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