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Cao C, Xiao A, Cai M, Shen B, Guo L, Shi X, Tian J, Hu Z. Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography. BIOMEDICAL OPTICS EXPRESS 2022; 13:6284-6299. [PMID: 36589575 PMCID: PMC9774866 DOI: 10.1364/boe.474982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/26/2022] [Accepted: 10/30/2022] [Indexed: 06/17/2023]
Abstract
Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference. An excitation-based fully connected network was proposed to model the inverse process of NIR-II photon propagation and directly obtain the 3D distribution of the light source. An excitation block was embedded in the network allowing it to autonomously pay more attention to neurons related to the light source. The barycenter error was added to the loss function to improve the localization accuracy of the light source. Both numerical simulation and in vivo experiments showed the superiority of the novel NIR-II FMT reconstruction strategy over the baseline methods. This strategy was expected to facilitate the application of machine learning in biomedical research.
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Affiliation(s)
- Caiguang Cao
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- These authors contributed equally
| | - Anqi Xiao
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- These authors contributed equally
| | - Meishan Cai
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Biluo Shen
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lishuang Guo
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, 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, School of Engineering Medicine, Beihang University, Beijing, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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2
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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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3
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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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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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6
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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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Improved reconstruction quality of bioluminescent images by combining SP(3) equations and Bregman iteration method. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:767296. [PMID: 23401723 PMCID: PMC3564267 DOI: 10.1155/2013/767296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Accepted: 12/24/2012] [Indexed: 11/23/2022]
Abstract
Bioluminescence tomography (BLT) has a great potential to provide a powerful tool for tumor detection, monitoring tumor therapy progress, and drug development; developing new reconstruction algorithms will advance the technique to practical applications. In the paper, we propose a BLT reconstruction algorithm by combining SP3 equations and Bregman iteration method to improve the quality of reconstructed sources. The numerical results for homogeneous and heterogeneous phantoms are very encouraging and give significant improvement over the algorithms without the use of SP3 equations and Bregman iteration method.
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Basevi HRA, Tichauer KM, Leblond F, Dehghani H, Guggenheim JA, Holt RW, Styles IB. Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise. BIOMEDICAL OPTICS EXPRESS 2012; 3:2131-41. [PMID: 23024907 PMCID: PMC3447555 DOI: 10.1364/boe.3.002131] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2012] [Revised: 06/27/2012] [Accepted: 07/04/2012] [Indexed: 05/08/2023]
Abstract
Bioluminescence Tomography attempts to quantify 3-dimensional luminophore distributions from surface measurements of the light distribution. The reconstruction problem is typically severely under-determined due to the number and location of measurements, but in certain cases the molecules or cells of interest form localised clusters, resulting in a distribution of luminophores that is spatially sparse. A Conjugate Gradient-based reconstruction algorithm using Compressive Sensing was designed to take advantage of this sparsity, using a multistage sparsity reduction approach to remove the need to choose sparsity weighting a priori. Numerical simulations were used to examine the effect of noise on reconstruction accuracy. Tomographic bioluminescence measurements of a Caliper XPM-2 Phantom Mouse were acquired and reconstructions from simulation and this experimental data show that Compressive Sensing-based reconstruction is superior to standard reconstruction techniques, particularly in the presence of noise.
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Affiliation(s)
- Hector R. A. Basevi
- PSIBS, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
| | - Kenneth M. Tichauer
- Thayer School of Engineering, Dartmouth College, 8000 Cummings Hall, Hanover, New Hampshire 03755,
USA
| | - Frederic Leblond
- Thayer School of Engineering, Dartmouth College, 8000 Cummings Hall, Hanover, New Hampshire 03755,
USA
| | - Hamid Dehghani
- PSIBS, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
- Thayer School of Engineering, Dartmouth College, 8000 Cummings Hall, Hanover, New Hampshire 03755,
USA
| | - James A. Guggenheim
- PSIBS, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
| | - Robert W. Holt
- Department of Physics & Astronomy, Dartmouth College, Hanover, New Hampshire 03755,
USA
| | - Iain B. Styles
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
UK
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Feng J, Qin C, Jia K, Zhu S, Liu K, Han D, Yang X, Gao Q, Tian J. Total variation regularization for bioluminescence tomography with the split Bregman method. APPLIED OPTICS 2012; 51:4501-12. [PMID: 22772124 DOI: 10.1364/ao.51.004501] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2011] [Accepted: 05/17/2012] [Indexed: 05/18/2023]
Abstract
Regularization methods have been broadly applied to bioluminescence tomography (BLT) to obtain stable solutions, including l2 and l1 regularizations. However, l2 regularization can oversmooth reconstructed images and l1 regularization may sparsify the source distribution, which degrades image quality. In this paper, the use of total variation (TV) regularization in BLT is investigated. Since a nonnegativity constraint can lead to improved image quality, the nonnegative constraint should be considered in BLT. However, TV regularization with a nonnegativity constraint is extremely difficult to solve due to its nondifferentiability and nonlinearity. The aim of this work is to validate the split Bregman method to minimize the TV regularization problem with a nonnegativity constraint for BLT. The performance of split Bregman-resolved TV (SBRTV) based BLT reconstruction algorithm was verified with numerical and in vivo experiments. Experimental results demonstrate that the SBRTV regularization can provide better regularization quality over l2 and l1 regularizations.
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Affiliation(s)
- Jinchao Feng
- College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China
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Chen X, Yang D, Qu X, Hu H, Liang J, Gao X, Tian J. Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection. JOURNAL OF BIOMEDICAL OPTICS 2012; 17:066015. [PMID: 22734771 DOI: 10.1117/1.jbo.17.6.066015] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.
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Affiliation(s)
- Xueli Chen
- Xidian University, School of Life Sciences and Technology, Xi'an 710126, China
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