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Cheng X, Sun S, Xiao Y, Li W, Li J, Yu J, Guo H. Fluorescence molecular tomography based on an online maximum a posteriori estimation algorithm. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2024; 41:844-851. [PMID: 38856571 DOI: 10.1364/josaa.519667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 03/25/2024] [Indexed: 06/11/2024]
Abstract
Fluorescence molecular tomography (FMT) is a non-invasive, radiation-free, and highly sensitive optical molecular imaging technique for early tumor detection. However, inadequate measurement information along with significant scattering of near-infrared light within the tissue leads to high ill-posedness in the inverse problem of FMT. To improve the quality and efficiency of FMT reconstruction, we build a reconstruction model based on log-sum regularization and introduce an online maximum a posteriori estimation (OPE) algorithm to solve the non-convex optimization problem. The OPE algorithm approximates a stationary point by evaluating the gradient of the objective function at each iteration, and its notable strength lies in the remarkable speed of convergence. The results of simulations and experiments demonstrate that the OPE algorithm ensures good reconstruction quality and exhibits outstanding performance in terms of reconstruction efficiency.
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Ran C, Pu K. Molecularly generated light and its biomedical applications. Angew Chem Int Ed Engl 2024; 63:e202314468. [PMID: 37955419 DOI: 10.1002/anie.202314468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/01/2023] [Accepted: 11/10/2023] [Indexed: 11/14/2023]
Abstract
Molecularly generated light, referred to here as "molecular light", mainly includes bioluminescence, chemiluminescence, and Cerenkov luminescence. Molecular light possesses unique dual features of being both a molecule and a source of light. Its molecular nature enables it to be delivered as molecules to regions deep within the body, overcoming the limitations of natural sunlight and physically generated light sources like lasers and LEDs. Simultaneously, its light properties make it valuable for applications such as imaging, photodynamic therapy, photo-oxidative therapy, and photobiomodulation. In this review article, we provide an updated overview of the diverse applications of molecular light and discuss the strengths and weaknesses of molecular light across various domains. Lastly, we present forward-looking perspectives on the potential of molecular light in the realms of molecular imaging, photobiological mechanisms, therapeutic applications, and photobiomodulation. While some of these perspectives may be considered bold and contentious, our intent is to inspire further innovations in the field of molecular light applications.
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Affiliation(s)
- Chongzhao Ran
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02129, USA
| | - Kanyi Pu
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 637459, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, 308232, Singapore, Singapore
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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 J, Zhang G, Chen Y, Li K, Zhao F, Yi H, Su L, Cao X. Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography. Phys Med Biol 2023; 68:195004. [PMID: 37647921 DOI: 10.1088/1361-6560/acf55a] [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: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 09/01/2023]
Abstract
Objective.Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem.Approach.To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well asin vivoexperiments.Main results.The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution.Siginificance.These findings indicate that the SCAD-GML method has the potential to advance the application of FMT inin vivobiological research.
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Affiliation(s)
- Jun Zhang
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Gege Zhang
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Yi Chen
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Kang Li
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Fengjun Zhao
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
| | - Huangjian Yi
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
| | - Linzhi Su
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Xin Cao
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
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Cao X, Li W, Chen Y, Du M, Zhang G, Zhang J, Li K, Su L. K-CapsNet: K-Nearest Neighbor Based Convolution Capsule Network for Cerenkov Luminescence Tomography Reconstruction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082846 DOI: 10.1109/embc40787.2023.10341089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Cerenkov luminescence tomography (CLT) has received significant attention as a promising imaging modality that can display the three-dimensional (3D) distribution of radioactive probes. However, the reconstruction of CLT suffers from severe ill-posed problem. It is difficult for traditional model-based method to obtain satisfactory result. Recently, deep learning-based method have shown great potential for accurate and efficient CLT reconstruction. In this study, a KNN-based convolution capsule network, named K-CapsNet, is proposed for cerenkov luminescence tomography. In K-CapsNet, the surface photon intensity is encoded in capsule form. The KNN-based convolution and K-means clustering are proposed for efficient encoding. Numerical simulation experiments have been carried out to verify the performance of K-CapsNet, and the results show that it performs superior in source localization and morphological restoration compared with existing methods.
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Yuan Y, Yi H, Kang D, Yu J, Guo H, He X, He X. Robust transformed l 1 metric for fluorescence molecular tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107503. [PMID: 37015182 DOI: 10.1016/j.cmpb.2023.107503] [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: 07/27/2022] [Revised: 02/27/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVE Fluorescence molecular tomography (FMT) is a non-invasive molecular imaging modality that can be used to observe the three-dimensional distribution of fluorescent probes in vivo. FMT is a promising imaging technique in clinical and preclinical research that has attracted significant attention. Numerous regularization based reconstruction algorithms have been proposed. However, traditional algorithms that use the squared l2-norm distance usually exaggerate the influence of noise and measurement and calculation errors, and their robustness cannot be guaranteed. METHODS In this study, we propose a novel robust transformed l1 (TL1) metric that interpolates l0 and l1 norms through a nonnegative parameter α∈(0,+∞). The TL1 metric looks like the lp-norm with p∈(0,1). These are markedly different because TL1 metric has two properties, boundedness and Lipschitz-continuity, which make the TL1 criterion suitable distance metric, particularly for robustness, owing to its stronger noise suppression. Subsequently, we apply the proposed metric to FMT and build a robust model to reduce the influence of noise. The nonconvexity of the proposed model made direct optimization difficult, and a continuous optimization method was developed to solve the model. The problem was converted into a difference in convex programming problem for the TL1 metric (DCATL1), and the corresponding algorithm converged linearly. RESULTS Various numerical simulations and in vivo bead-implanted mouse experiments were conducted to verify the performance of the proposed method. The experimental results show that the DCATL1 algorithm is more robust than the state-of-the-art approaches and achieves better source localization and morphology recovery. CONCLUSIONS The in vivo experiments showed that DCATL1 can be used to visualize the distribution of fluorescent probes inside biological tissues and promote preclinical application in small animals, demonstrating the feasibility and effectiveness of the proposed method.
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Affiliation(s)
- Yating Yuan
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - 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, 710127, China
| | - Dizhen Kang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710119, China
| | - Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China; School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - 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, 710127, China.
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An Y, Wang H, Li J, Li G, Ma X, Du Y, Tian J. Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:2225-2239. [PMID: 37206151 PMCID: PMC10191665 DOI: 10.1364/boe.486451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/27/2023] [Indexed: 05/21/2023]
Abstract
Fluorescence molecular tomography can combine two-dimensional fluorescence imaging with anatomical information to reconstruct three-dimensional images of tumors. Reconstruction based on traditional regularization with tumor sparsity priors does not take into account that tumor cells form clusters, so it performs poorly when multiple light sources are used. Here we describe reconstruction based on an "adaptive group least angle regression elastic net" (AGLEN) method, in which local spatial structure correlation and group sparsity are integrated with elastic net regularization, followed by least angle regression. The AGLEN method works iteratively using the residual vector and a median smoothing strategy in order to adaptively obtain a robust local optimum. The method was verified using numerical simulations as well as imaging of mice bearing liver or melanoma tumors. AGLEN reconstruction performed better than state-of-the-art methods with different sizes of light sources at different distances from the sample and in the presence of Gaussian noise at 5-25%. In addition, AGLEN-based reconstruction accurately imaged tumor expression of cell death ligand-1, which can guide immunotherapy.
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Affiliation(s)
- Yu An
- the Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, School of Engineering Medicine, Beihang University, Beijing, 100191, China
| | - Hanfan Wang
- the CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jiaqian Li
- the Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, School of Engineering Medicine, Beihang University, Beijing, 100191, China
| | - Guanghui Li
- the Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, School of Engineering Medicine, Beihang University, Beijing, 100191, China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Yang Du
- the CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jie Tian
- the Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, School of Engineering Medicine, Beihang University, Beijing, 100191, China
- the CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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8
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Li J, Zhang L, Liu J, Zhang D, Kang D, Wang B, He X, Zhang H, Zhao Y, Guo H, Hou Y. An adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction. JOURNAL OF BIOPHOTONICS 2023:e202300031. [PMID: 37074336 DOI: 10.1002/jbio.202300031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/20/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
To alleviate the ill-posed of the inverse problem in fluorescent molecular tomography (FMT), many regularization methods based on L2 or L1 norm have been proposed. Whereas, the quality of regularization parameters affects the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L2 or L1 norm and achieve good reconstruction performance.
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Affiliation(s)
- Jintao Li
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Lizhi Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Jia Liu
- Xi'an Company of Shaanxi Tobacco Company, The Information Center, Xi'an, China
| | - Diya Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Dizhen Kang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Beilei Wang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 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
| | - Heng Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Yizhe Zhao
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, 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
| | - Yuqing Hou
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
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Chen Y, Li W, Du M, Su L, Yi H, Zhao F, Li K, Wang L, Cao X. Elastic net-based non-negative iterative three-operator splitting strategy for Cerenkov luminescence tomography. OPTICS EXPRESS 2022; 30:35282-35299. [PMID: 36258483 DOI: 10.1364/oe.465501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/18/2022] [Indexed: 06/16/2023]
Abstract
Cerenkov luminescence tomography (CLT) provides a powerful optical molecular imaging technique for non-invasive detection and visualization of radiopharmaceuticals in living objects. However, the severe photon scattering effect causes ill-posedness of the inverse problem, and the location accuracy and shape recovery of CLT reconstruction results are unsatisfactory for clinical application. Here, to improve the reconstruction spatial location accuracy and shape recovery ability, a non-negative iterative three operator splitting (NNITOS) strategy based on elastic net (EN) regularization was proposed. NNITOS formalizes the CLT reconstruction as a non-convex optimization problem and splits it into three operators, the least square, L1/2-norm regularization, and adaptive grouping manifold learning, then iteratively solved them. After stepwise iterations, the result of NNITOS converged progressively. Meanwhile, to speed up the convergence and ensure the sparsity of the solution, shrinking the region of interest was utilized in this strategy. To verify the effectiveness of the method, numerical simulations and in vivo experiments were performed. The result of these experiments demonstrated that, compared to several methods, NNITOS can achieve superior performance in terms of location accuracy, shape recovery capability, and robustness. We hope this work can accelerate the clinical application of CLT in the future.
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Bianfei S, Fang L, Zhongzheng X, Yuanyuan Z, Tian Y, Tao H, Jiachun M, Xiran W, Siting Y, Lei L. Application of Cherenkov radiation in tumor imaging and treatment. Future Oncol 2022; 18:3101-3118. [PMID: 36065976 DOI: 10.2217/fon-2022-0022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Cherenkov radiation (CR) is the characteristic blue glow that is generated during radiotherapy or radioisotope decay. Its distribution and intensity naturally reflect the actual dose and field of radiotherapy and the location of radioisotope imaging agents in vivo. Therefore, CR can represent a potential in situ light source for radiotherapy monitoring and radioisotope-based tumor imaging. When used in combination with new imaging techniques, molecular probes or nanomedicine, CR imaging exhibits unique advantages (accuracy, low cost, convenience and fast) in tumor radiotherapy monitoring and imaging. Furthermore, photosensitive nanomaterials can be used for CR photodynamic therapy, providing new approaches for integrating tumor imaging and treatment. Here the authors review the latest developments in the use of CR in tumor research and discuss current challenges and new directions for future studies.
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Affiliation(s)
- Shao Bianfei
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Liu Fang
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiation Oncology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiang Zhongzheng
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Zeng Yuanyuan
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Tian
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - He Tao
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Ma Jiachun
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Wang Xiran
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Siting
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Liu Lei
- Department of Head and Neck Oncology, West China Hospital, Sichuan University, Chengdu, China
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Subasinghe SAAS, Pautler RG, Samee MAH, Yustein JT, Allen MJ. Dual-Mode Tumor Imaging Using Probes That Are Responsive to Hypoxia-Induced Pathological Conditions. BIOSENSORS 2022; 12:478. [PMID: 35884281 PMCID: PMC9313010 DOI: 10.3390/bios12070478] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/22/2022] [Accepted: 06/26/2022] [Indexed: 05/02/2023]
Abstract
Hypoxia in solid tumors is associated with poor prognosis, increased aggressiveness, and strong resistance to therapeutics, making accurate monitoring of hypoxia important. Several imaging modalities have been used to study hypoxia, but each modality has inherent limitations. The use of a second modality can compensate for the limitations and validate the results of any single imaging modality. In this review, we describe dual-mode imaging systems for the detection of hypoxia that have been reported since the start of the 21st century. First, we provide a brief overview of the hallmarks of hypoxia used for imaging and the imaging modalities used to detect hypoxia, including optical imaging, ultrasound imaging, photoacoustic imaging, single-photon emission tomography, X-ray computed tomography, positron emission tomography, Cerenkov radiation energy transfer imaging, magnetic resonance imaging, electron paramagnetic resonance imaging, magnetic particle imaging, and surface-enhanced Raman spectroscopy, and mass spectrometric imaging. These overviews are followed by examples of hypoxia-relevant imaging using a mixture of probes for complementary single-mode imaging techniques. Then, we describe dual-mode molecular switches that are responsive in multiple imaging modalities to at least one hypoxia-induced pathological change. Finally, we offer future perspectives toward dual-mode imaging of hypoxia and hypoxia-induced pathophysiological changes in tumor microenvironments.
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Affiliation(s)
| | - Robia G. Pautler
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA; (R.G.P.); (M.A.H.S.)
| | - Md. Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA; (R.G.P.); (M.A.H.S.)
| | - Jason T. Yustein
- Integrative Molecular and Biomedical Sciences and the Department of Pediatrics in the Texas Children’s Cancer and Hematology Centers and The Faris D. Virani Ewing Sarcoma Center, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Matthew J. Allen
- Department of Chemistry, Wayne State University, 5101 Cass Avenue, Detroit, MI 48202, USA;
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Abstract
Malignant tumors rank as a leading cause of death worldwide. Accurate diagnosis and advanced treatment options are crucial to win battle against tumors. In recent years, Cherenkov luminescence (CL) has shown its technical advantages and clinical transformation potential in many important fields, particularly in tumor diagnosis and treatment, such as tumor detection in vivo, surgical navigation, radiotherapy, photodynamic therapy, and the evaluation of therapeutic effect. In this review, we summarize the advances in CL for tumor diagnosis and treatment. We first describe the physical principles of CL and discuss the imaging techniques used in tumor diagnosis, including CL imaging, CL endoscope, and CL tomography. Then we present a broad overview of the current status of surgical resection, radiotherapy, photodynamic therapy, and tumor microenvironment monitoring using CL. Finally, we shed light on the challenges and possible solutions for tumor diagnosis and therapy using CL.
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Guo H, Yu J, He X, Yi H, Hou Y, He X. Total Variation Constrained Graph Manifold Learning Strategy for Cerenkov Luminescence Tomography. OPTICS EXPRESS 2022; 30:1422-1441. [PMID: 35209303 DOI: 10.1364/oe.448250] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/19/2021] [Indexed: 06/14/2023]
Abstract
Harnessing the power and flexibility of radiolabeled molecules, Cerenkov luminescence tomography (CLT) provides a novel technique for non-invasive visualisation and quantification of viable tumour cells in a living organism. However, owing to the photon scattering effect and the ill-posed inverse problem, CLT still suffers from insufficient spatial resolution and shape recovery in various preclinical applications. In this study, we proposed a total variation constrained graph manifold learning (TV-GML) strategy for achieving accurate spatial location, dual-source resolution, and tumour morphology. TV-GML integrates the isotropic total variation term and dynamic graph Laplacian constraint to make a trade-off between edge preservation and piecewise smooth region reconstruction. Meanwhile, the tetrahedral mesh-Cartesian grid pair method based on the k-nearest neighbour, and the adaptive and composite Barzilai-Borwein method, were proposed to ensure global super linear convergence of the solution of TV-GML. The comparison results of both simulation experiments and in vivo experiments further indicated that TV-GML achieved superior reconstruction performance in terms of location accuracy, dual-source resolution, shape recovery capability, robustness, and in vivo practicability. Significance: We believe that this novel method will be beneficial to the application of CLT for quantitative analysis and morphological observation of various preclinical applications and facilitate the development of the theory of solving inverse problem.
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14
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Zhang H, He X, Yu J, He X, Guo H, Hou Y. L1-L2 norm regularization via forward-backward splitting for fluorescence molecular tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:7807-7825. [PMID: 35003868 PMCID: PMC8713696 DOI: 10.1364/boe.435932] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 11/08/2021] [Accepted: 11/08/2021] [Indexed: 05/07/2023]
Abstract
Fluorescent molecular tomography (FMT) is a highly sensitive and noninvasive imaging approach for providing three-dimensional distribution of fluorescent marker probes. However, owing to its light scattering effect and the ill-posedness of inverse problems, it is challenging to develop an efficient reconstruction algorithm that can achieve the exact location and morphology of the fluorescence source. In this study, therefore, in order to satisfy the need for early tumor detection and improve the sparsity of solution, we proposed a novel L 1-L 2 norm regularization via the forward-backward splitting method for enhancing the FMT reconstruction accuracy and the robustness. By fully considering the highly coherent nature of the system matrix of FMT, it operates by splitting the objective to be minimized into simpler functions, which are dealt with individually to obtain a sparser solution. An analytic solution of L 1-L 2 norm proximal operators and a forward-backward splitting algorithm were employed to efficiently solve the nonconvex L 1-L 2 norm minimization problem. Numerical simulations and an in-vivo glioma mouse model experiment were conducted to evaluate the performance of our algorithm. The comparative results of these experiments demonstrated that the proposed algorithm obtained superior reconstruction performance in terms of spatial location, dual-source resolution, and in-vivo practicability. It was believed that this study would promote the preclinical and clinical applications of FMT in early tumor detection.
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Affiliation(s)
- Heng Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - 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, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Yuqing Hou
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
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15
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Zhang X, Cai M, Guo L, Zhang Z, Shen B, Zhang X, Hu Z, Tian J. Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:7703-7716. [PMID: 35003861 PMCID: PMC8713679 DOI: 10.1364/boe.443517] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/28/2021] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
Cerenkov luminescence tomography (CLT) is a novel and highly sensitive imaging technique, which could obtain the three-dimensional distribution of radioactive probes to achieve accurate tumor detection. However, the simplified radiative transfer equation and ill-conditioned inverse problem cause a reconstruction error. In this study, a novel attention mechanism based locally connected (AMLC) network was proposed to reduce barycenter error and improve morphological restorability. The proposed AMLC network consisted of two main parts: a fully connected sub-network for providing a coarse reconstruction result, and a locally connected sub-network based on an attention matrix for refinement. Both numerical simulations and in vivo experiments were conducted to show the superiority of the AMLC network in accuracy and stability over existing methods (MFCNN, KNN-LC network). This method improved CLT reconstruction performance and promoted the application of machine learning in optical imaging research.
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Affiliation(s)
- Xiaoning Zhang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Equal contribution
| | - Meishan Cai
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Equal contribution
| | - Lishuang Guo
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zeyu Zhang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Biluo Shen
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaojun Zhang
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, 100853, China
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, 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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16
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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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17
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Zhang H, Guo H, Li S, Liu Y, He X, He X, Hou Y. L 1-L 2 Minimization Via A Proximal Operator For Fluorescence Molecular Tomography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3640-3645. [PMID: 34892026 DOI: 10.1109/embc46164.2021.9630236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Fluorescent Molecular Tomography (FMT) is a highly sensitive and noninvasive imaging method that provides three-dimensional distribution of biomarkers by noninvasive detection of fluorescent marker probes. However, due to the light scattering effect and ill-posedness of inverse problems, it is challenging to develop an efficient construction method that can provide the exact location and morphology of the fluorescence distribution. In this paper, we proposed L1-L2 norm regularization to improve FMT reconstruction. In our research, proximal operators of non-convex L1 -L2 norm and forward-backward splitting method was adopted to solve the inverse problem of FMT. Simulation results on heterogeneous mouse model demonstrated that the proposed FBS method is superior to IVTCG, DCA and IRW-L1/2 reconstruction methods in location accuracy and other aspects.
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18
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Guo H, Zhao H, Yu J, He X, He X, Song X. X-ray luminescence computed tomography using a hybrid proton propagation model and Lasso-LSQR algorithm. JOURNAL OF BIOPHOTONICS 2021; 14:e202100089. [PMID: 34176239 DOI: 10.1002/jbio.202100089] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 06/13/2023]
Abstract
X-ray luminescence computed tomography (XLCT) uses external X-rays for luminescence excitation, which is becoming a promising molecular imaging technique with superb penetration depth and spatial resolution. To achieve the tomographic mapping of luminescence distribution, accurate optical propagation model and suitable reconstruction method are two keys for XLCT, but not satisfied. To overcome the limitation of the single proton propagation model (e.g., DE, SP3 ), we adopted a hybrid diffusion equation with third order simplified spherical harmonics (DE-SP3 ) model for XLCT. To enable fast iteration and accurate sparse reconstruction, we also integrated in the inversion optimization, with a novel Least Square QR-factorization based on the Lasso (Lasso-LSQR) algorithm. We first simulated the light propagation in various kinds of organs under DE model and SP3 model, respectively. By comparison with the Monte Carlo, these tissues can be categorized into two types, namely DE-fitted tissues that include muscle and lung, and SP3 -fitted tissues including heart, kidney, liver, and stomach. According to the above classification results, we built a hybrid DE-SP3 model to more accurately describing light transport. Numerical simulations and in vivo experiments illustrated that hybrid DE-SP3 model achieves superior reconstruction performance in terms of location accuracy, and spatial resolution than DE, and less computational cost than SP3 . The hybrid DE-SP3 model materializes a balance between accuracy and efficiency for XLCT.
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Affiliation(s)
- Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Hengna Zhao
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Jingjing Yu
- The School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Xiaowei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Xiaolei Song
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The School of Information Sciences and Technology, Northwest University, Xi'an, China
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19
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Wang L, He X, Yu J. Prior Compensation Algorithm for Cerenkov Luminescence Tomography From Single-View Measurements. Front Oncol 2021; 11:749889. [PMID: 34631587 PMCID: PMC8495210 DOI: 10.3389/fonc.2021.749889] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Cerenkov luminescence tomography (CLT) has attracted much attention because of the wide clinically-used probes and three-dimensional (3D) quantification ability. However, due to the serious morbidity of 3D optical imaging, the reconstructed images of CLT are not appreciable, especially when single-view measurements are used. Single-view CLT improves the efficiency of data acquisition. It is much consistent with the actual imaging environment of using commercial imaging system, but bringing the problem that the reconstructed results will be closer to the animal surface on the side where the single-view image is collected. To avoid this problem to the greatest extent possible, we proposed a prior compensation algorithm for CLT reconstruction based on depth calibration strategy. This method takes full account of the fact that the attenuation of light in the tissue will depend heavily on the depth of the light source as well as the distance between the light source and the detection plane. Based on this consideration, a depth calibration matrix was designed to calibrate the attenuation between the surface light flux and the density of the internal light source. The feature of the algorithm was that the depth calibration matrix directly acts on the system matrix of CLT reconstruction, rather than modifying the regularization penalty items. The validity and effectiveness of the proposed algorithm were evaluated with a numerical simulation and a mouse-based experiment, whose results illustrated that it located the radiation sources accurately by using single-view measurements.
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Affiliation(s)
- Lin Wang
- School of Information Sciences and Technology, Northwest University, Xi'an, China.,School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
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20
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Yuan Y, Guo H, Yi H, Yu J, He X, He X. Correntropy-induced metric with Laplacian kernel for robust fluorescence molecular tomography. BIOMEDICAL OPTICS EXPRESS 2021; 12:5991-6012. [PMID: 34745717 PMCID: PMC8547984 DOI: 10.1364/boe.434679] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/08/2021] [Accepted: 08/22/2021] [Indexed: 06/13/2023]
Abstract
Fluorescence molecular tomography (FMT), which is used to visualize the three-dimensional distribution of fluorescence probe in small animals via the reconstruction method, has become a promising imaging technique in preclinical research. However, the classical reconstruction criterion is formulated based on the squared l 2-norm distance metric, leaving it prone to being influenced by the presence of outliers. In this study, we propose a robust distance based on the correntropy-induced metric with a Laplacian kernel (CIML). The proposed metric satisfies the conditions of distance metric function and contains first and higher order moments of samples. Moreover, we demonstrate important properties of the proposed metric such as nonnegativity, nonconvexity, and boundedness, and analyze its robustness from the perspective of M-estimation. The proposed metric includes and extends the traditional metrics such as l 0-norm and l 1-norm metrics by setting an appropriate parameter. We show that, in reconstruction, the metric is a sparsity-promoting penalty. To reduce the negative effects of noise and outliers, a novel robust reconstruction framework is presented with the proposed correntropy-based metric. The proposed CIML model retains the advantages of the traditional model and promotes robustness. However, the nonconvexity of the proposed metric renders the CIML model difficult to optimize. Furthermore, an effective iterative algorithm for the CIML model is designed, and we present a theoretical analysis of its ability to converge. Numerical simulation and in vivo mouse experiments were conducted to evaluate the CIML method's performance. The experimental results show that the proposed method achieved more accurate fluorescent target reconstruction than the state-of-the-art methods in most cases, which illustrates the feasibility and robustness of the CIML method.
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Affiliation(s)
- Yating Yuan
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, 710127, China
| | - Hongbo Guo
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, 710127, China
| | - 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, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, 710119, China
| | - Xuelei He
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, 710127, China
| | - 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, 710127, China
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21
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Nakanishi K, Yamamoto S. Comparison of the distributions of bremsstrahlung X-rays, Cerenkov light, and annihilation radiations for positron emitters. Appl Radiat Isot 2021; 176:109861. [PMID: 34265565 DOI: 10.1016/j.apradiso.2021.109861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 07/03/2021] [Accepted: 07/09/2021] [Indexed: 11/27/2022]
Abstract
Positron emission tomography (PET) is a powerful tool because we can acquire functional information of tissue from the images with high sensitivity and relatively high spatial resolution. However, high-spatial-resolution PET imaging for high-energy positron emitters is difficult because the positrons have a long range and annihilation radiations are emitted at the endpoints of the positrons' trajectories. Along the trajectories, Cerenkov light (CL) is also emitted in advance of the emission of annihilation radiations. Hence, CL can be used for the imaging of high-energy positron emitters. Bremsstrahlung X-rays are also emitted along the trajectories of positrons, and imaging is possible. However, the differences in the spatial distributions of these three types of radiations are not obvious. Because CL and bremsstrahlung X-rays are produced before the endpoint of the positron, high-spatial-resolution imaging may be possible for high-energy positrons. In this study, to clarify this point, we simulated the spatial distribution of CL, bremsstrahlung X-rays, and annihilation radiations using Monte Carlo simulation and compared the distributions. The distributions of the bremsstrahlung X-rays and CL were smaller than those of the annihilation radiations in case of high energy positrons, and we found that the distributions of bremsstrahlung X-rays nearly matched those of CL for high-energy positron emitters. We concluded that CL and bremsstrahlung X-ray imaging have higher spatial resolution than annihilation radiation imaging for MeV ordered positron emitters, and thus they are promising for high-spatial-resolution imaging of high-energy positron emitters such as O-15 for ion therapy and Ga-68 for PET imaging.
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Affiliation(s)
- Kouhei Nakanishi
- Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan; Department of Radiology, Akita Hospital, Chiryu, Japan
| | - Seiichi Yamamoto
- Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.
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22
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Ren W, Cui S, Alini M, Grad S, Zhou Q, Li Z, Razansky D. Noninvasive multimodal fluorescence and magnetic resonance imaging of whole-organ intervertebral discs. BIOMEDICAL OPTICS EXPRESS 2021; 12:3214-3227. [PMID: 34221655 PMCID: PMC8221942 DOI: 10.1364/boe.421205] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/21/2021] [Accepted: 04/21/2021] [Indexed: 06/13/2023]
Abstract
Low back pain (LBP) is a commonly experienced symptom posing a tremendous healthcare burden to individuals and society at large. The LBP pathology is strongly linked to degeneration of the intervertebral disc (IVD), calling for development of early-stage diagnostic tools for visualizing biomolecular changes in IVD. Multimodal measurements of fluorescence molecular tomography (FMT) and magnetic resonance imaging (MRI) were performed on IVD whole organ culture model using an in-house built FMT system and a high-field MRI scanner. The resulted multimodal images were systematically validated through epifluorescence imaging of the IVD sections at a microscopic level. Multiple image contrasts were exploited, including fluorescence distribution, anatomical map associated with T1-weighted MRI contrast, and water content related with T2 relaxation time. The developed multimodality imaging approach may thus serve as a new assessment tool for early diagnosis of IVD degeneration and longitudinal monitoring of IVD organ culture status using fluorescence markers.
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Affiliation(s)
- Wuwei Ren
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, University of Zurich and ETH Zurich, 8093 Zurich, Switzerland
- equal contribution
| | - Shangbin Cui
- AO Research Institute Davos, 7270 Davos, Switzerland
- The First Affiliated Hospital of Sun Yat-sen University, 510080 Guangzhou, China
- equal contribution
| | - Mauro Alini
- AO Research Institute Davos, 7270 Davos, Switzerland
| | - Sibylle Grad
- AO Research Institute Davos, 7270 Davos, Switzerland
| | - Quanyu Zhou
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, University of Zurich and ETH Zurich, 8093 Zurich, Switzerland
| | - Zhen Li
- AO Research Institute Davos, 7270 Davos, Switzerland
| | - Daniel Razansky
- Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, University of Zurich and ETH Zurich, 8093 Zurich, Switzerland
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23
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Wang H, Bian C, Kong L, An Y, Du Y, Tian J. A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1484-1498. [PMID: 33556004 DOI: 10.1109/tmi.2021.3057704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Fluorescence molecular tomography (FMT) is a new type of medical imaging technology that can quantitatively reconstruct the three-dimensional distribution of fluorescent probes in vivo. Traditional Lp norm regularization techniques used in FMT reconstruction often face problems such as over-sparseness, over-smoothness, spatial discontinuity, and poor robustness. To address these problems, this paper proposes an adaptive parameter search elastic net (APSEN) method that is based on elastic net regularization, using weight parameters to combine the L1 and L2 norms. For the selection of elastic net weight parameters, this approach introduces the L0 norm of valid reconstruction results and the L2 norm of the residual vector, which are used to adjust the weight parameters adaptively. To verify the proposed method, a series of numerical simulation experiments were performed using digital mice with tumors as experimental subjects, and in vivo experiments of liver tumors were also conducted. The results showed that, compared with the state-of-the-art methods with different light source sizes or distances, Gaussian noise of 5%-25%, and the brute-force parameter search method, the APSEN method has better location accuracy, spatial resolution, fluorescence yield recovery ability, morphological characteristics, and robustness. Furthermore, the in vivo experiments demonstrated the applicability of APSEN for FMT.
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24
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Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks. Eur J Nucl Med Mol Imaging 2021; 48:3482-3492. [PMID: 33904984 PMCID: PMC8440289 DOI: 10.1007/s00259-021-05326-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/20/2021] [Indexed: 11/29/2022]
Abstract
Purpose Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon’s visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery. Methods Glioma patients (N = 23) were enrolled and injected with indocyanine green for fluorescence image–guided surgery. Tissue samples (N = 1874) were harvested from surgery of these patients, and the second near-infrared window (NIR-II, 1000–1700 nm) fluorescence images were obtained. Deep convolutional neural networks (CNNs) combined with NIR-II fluorescence imaging (named as FL-CNN) were explored to automatically provide pathological diagnosis of glioma in situ in real-time during patient surgery. The pathological examination results were used as the gold standard. Results The developed FL-CNN achieved the area under the curve (AUC) of 0.945. Comparing to neurosurgeons’ judgment, with the same level of specificity >80%, FL-CNN achieved a much higher sensitivity (93.8% versus 82.0%, P < 0.001) with zero time overhead. Further experiments demonstrated that FL-CNN corrected >70% of the errors made by neurosurgeons. FL-CNN was also able to rapidly predict grade and Ki-67 level (AUC 0.810 and 0.625) of tumor specimens intraoperatively. Conclusion Our study demonstrates that deep CNNs are better at capturing important information from fluorescence images than surgeons’ evaluation during patient surgery. FL-CNN is highly promising to provide pathological diagnosis intraoperatively and assist neurosurgeons to obtain maximum resection safely. Trial registration ChiCTR ChiCTR2000029402. Registered 29 January 2020, retrospectively registered Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05326-y.
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25
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Cao X, Zhang J, Yang J, Fan C, Zhao F, Zhou W, Wang L, Geng G, Zhou M, Chen X. A deep unsupervised clustering-based post-processing framework for high-fidelity Cerenkov luminescence tomography. JOURNAL OF APPLIED PHYSICS 2020; 128. [DOI: 10.1063/5.0025877] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
Cerenkov Luminescence Tomography (CLT) is a promising optical molecular imaging technology. It involves the three-dimensional reconstruction of the distribution of radionuclide probes inside a single object to indicate a tumor's localization and distribution. However, reconstruction using CLT suffers from severe ill-posedness, resulting in numerous artifacts within the reconstructed images. These artifacts influence the visual effect and may misguide the medical professional (diagnostician), resulting in a wrong diagnosis. Here, we proposed a deep unsupervised clustering-based post-processing framework to eliminate artifacts and facilitate high-fidelity CLT. First, an initial reconstructed image was obtained by a specific reconstruction method. Second, voxel data were generated based on the initial reconstructed result. Third, these voxels were divided into three groups, and only the group with the highest mean intensity was chosen as the final reconstructed result. A group of numerical simulation and in vivo mouse-based experiments were conducted to assess the presented framework's feasibility and potential. The results indicated that the proposed framework could reduce the number of artifacts effectively. The reconstructed image's shape and distribution were more similar to the actual light source than those obtained without the proposed framework.
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Affiliation(s)
- Xin Cao
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Jun Zhang
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Jianan Yang
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Chunxiao Fan
- School of Computer Science and Information Engineering, Hefei University of Technology 2 , Hefei, Anhui 230601, China
| | - Fengjun Zhao
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Wei Zhou
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Lin Wang
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Guohua Geng
- School of Information Sciences and Technology, Northwest University 1 , Xi'an, Shaanxi 710127, China
| | - Mingquan Zhou
- Engineering Research Center of Virtual Reality and Applications, Ministry of Education, Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing Normal University 3 , Beijing, China
| | - Xueli Chen
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education & School of Life Science and Technology, Xidian University 4 , Xi'an, Shaanxi 710071, China
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Cai M, Zhang Z, Shi X, Yang J, Hu Z, Tian J. Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3207-3217. [PMID: 32324543 DOI: 10.1109/tmi.2020.2987640] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Cerenkov luminescence tomography (CLT) is a promising imaging tool for obtaining three-dimensional (3D) non-invasive visualization of the in vivo distribution of radiopharmaceuticals. However, the reconstruction performance remains unsatisfactory for biomedical applications because the inverse problem of CLT is severely ill-conditioned and intractable. In this study, therefore, a novel non-negative iterative convex refinement (NNICR) approach was utilized to improve the CLT reconstruction accuracy, robustness as well as the shape recovery capability. The spike and slab prior information was employed to capture the sparsity of Cerenkov source, which could be formalized as a non-convex optimization problem. The NNICR approach solved this non-convex problem by refining the solutions of the convex sub-problems. To evaluate the performance of the NNICR approach, numerical simulations and in vivo tumor-bearing mice models experiments were conducted. Conjugated gradient based Tikhonov regularization approach (CG-Tikhonov), fast iterative shrinkage-thresholding algorithm based Lasso approach (Fista-Lasso) and Elastic-Net regularization approach were used for the comparison of the reconstruction performance. The results of these experiments demonstrated that the NNICR approach obtained superior reconstruction performance in terms of location accuracy, shape recovery capability, robustness and in vivo practicability. It was believed that this study would facilitate the preclinical and clinical applications of CLT in the future.
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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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Guo H, Gao L, Yu J, He X, Wang H, Zheng J, Yang X. Sparse-graph manifold learning method for bioluminescence tomography. JOURNAL OF BIOPHOTONICS 2020; 13:e201960218. [PMID: 31990430 DOI: 10.1002/jbio.201960218] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 01/09/2020] [Accepted: 01/15/2020] [Indexed: 06/10/2023]
Abstract
In preclinical researches, bioluminescence tomography (BLT) has widely been used for tumor imaging and monitoring, imaged-guided tumor therapy, and so forth. For these biological applications, both tumor spatial location and morphology analysis are the leading problems needed to be taken into account. However, most existing BLT reconstruction methods were proposed for some specific applications with a focus on sparse representation or morphology recovery, respectively. How to design a versatile algorithm that can simultaneously deal with both aspects remains an impending problem. In this study, a Sparse-Graph Manifold Learning (SGML) method was proposed to balance the source sparseness and morphology, by integrating non-convex sparsity constraint and dynamic Laplacian graph model. Furthermore, based on the nonconvex optimization theory and some iterative approximation, we proposed a novel iteratively reweighted soft thresholding algorithm (IRSTA) to solve the SGML model. Numerical simulations and in vivo experiments result demonstrated that the proposed SGML method performed much superior to the comparative methods in spatial localization and tumor morphology recovery for various source settings. It is believed that the SGML method can be applied to the related optical tomography and facilitate the development of optical molecular tomography.
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Affiliation(s)
- Hongbo Guo
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
| | - Ling Gao
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Xi'an Polytechnic University, Xi'an, China
| | - Jingjing Yu
- The School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
| | - Hai Wang
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
| | - Jie Zheng
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
| | - Xudong Yang
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
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Zhang Z, Qu Y, Cao Y, Shi X, Guo H, Zhang X, Zheng S, Liu H, Hu Z, Tian J. A novel in vivo Cerenkov luminescence image-guided surgery on primary and metastatic colorectal cancer. JOURNAL OF BIOPHOTONICS 2020; 13:e201960152. [PMID: 31800171 DOI: 10.1002/jbio.201960152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/01/2019] [Accepted: 12/03/2019] [Indexed: 06/10/2023]
Abstract
Intraoperative Cerenkov luminescence imaging (CLI) can effectively improve the performance of tumor surgery. Nevertheless, the existing approaches are still unsatisfying to the clinical demands of open surgery. This study develops a novel intraoperative in vivo CLI approach to investigate the potential and value of Cerenkov luminescence (CL) image-guided surgery. A system characterized with high sensitivity (19.61 kBq mL-1 18 F-FDG) and desirable spatial resolution (88.34 μm) is developed. CL image-guided surgery is performed on colorectal cancer (CRC) models of mice and swine. Tumor surgery is guided by the static CL images, and the resection quality is evaluated quantitatively and contrasted with other imaging modalities exemplified by bioluminescence imaging (BLI). The in vivo results demonstrated the effectiveness of the proposed intraoperative CLI approach for removing primary and metastatic CRC. Safety of performing in vivo CL image-guided surgery is verified as well through radiation measurements of related staffs. Overall, the developed intraoperative in vivo CLI approach can efficiently improve the cancer treatment.
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Affiliation(s)
- Zeyu Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yawei Qu
- Department of Gastroenterology, the Third Medical Centre, Chinese PLA General Hospital, Beijing, China
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China
| | - Yu Cao
- Department of Anorectal, the Third medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hongbo Guo
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Xiaojun Zhang
- Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing, China
| | - Sheng Zheng
- Department of Gastroenterology, the Third Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Haifeng Liu
- Department of Gastroenterology, the Third Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
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30
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Kong L, An Y, Liang Q, Yin L, Du Y, Tian J. Reconstruction for Fluorescence Molecular Tomography via Adaptive Group Orthogonal Matching Pursuit. IEEE Trans Biomed Eng 2020; 67:2518-2529. [PMID: 31905129 DOI: 10.1109/tbme.2019.2963815] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Fluorescence molecular tomography (FMT) is a promising medical imaging technology aimed at the non-invasive, specific, and sensitive detection of the distribution of fluorophore. Conventional sparsity prior-based methods of FMT commonly face problems such as over-sparseness, spatial discontinuity, and poor robustness, due to the neglect of the interrelation within the local subspace. To address this, we propose an adaptive group orthogonal matching pursuit (AGOMP) method. METHODS AGOMP is based on a novel local spatial-structured sparse regularization, which leverages local spatial interrelations as group sparsity without the hard prior of the tumor region. The adaptive grouped subspace matching pursuit method was adopted to enhance the interrelatedness of elements within a group, which alleviates the over-sparsity problem to some extent and improves the accuracy, robustness, and morphological similarity of FMT reconstruction. A series of numerical simulation experiments, based on digital mouse with both one and several tumors, were conducted, as well as in vivo mouse experiments. RESULTS The results demonstrated that the proposed AGOMP method achieved better location accuracy, fluorescent yield reconstruction, relative sparsity, and morphology than state-of-the-art methods under complex conditions for levels of Gaussian noise ranging from 5-25%. Furthermore, the in vivo mouse experiments demonstrated the practical application of FMT with AGOMP. CONCLUSION The proposed AGOMP can improve the accuracy and robustness for FMT reconstruction in biomedical application.
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Zhang Z, Cai M, Gao Y, Shi X, Zhang X, Hu Z, Tian J. A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network. Phys Med Biol 2019; 64:245010. [PMID: 31770734 DOI: 10.1088/1361-6560/ab5bb4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cerenkov luminescence tomography (CLT) has been proved as an effective tool for various biomedical applications. Because of the severe scattering of Cerenkov luminescence, the performance of CLT remains unsatisfied. This paper proposed a novel CLT reconstruction approach based on a multilayer fully connected neural network (MFCNN). Monte Carlo simulation data was employed to train the MFCNN, and the complex relationship between the surface signals and the true sources was effectively learned by the network. Both simulation and in vivo experiments were performed to validate the performance of MFCNN CLT, and it was further compared with the typical radiative transfer equation (RTE) based method. The experimental data showed the superiority of MFCNN CLT in terms of accuracy and stability. This promising approach for CLT is expected to improve the performance of optical tomography, and to promote the exploration of machine learning in biomedical applications.
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Affiliation(s)
- Zeyu Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710126, People's Republic of China. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China. These authors contributed equally to this study
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32
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Zhang Z, Cai M, Bao C, Hu Z, Tian J. Endoscopic Cerenkov luminescence imaging and image-guided tumor resection on hepatocellular carcinoma-bearing mouse models. NANOMEDICINE-NANOTECHNOLOGY BIOLOGY AND MEDICINE 2019; 17:62-70. [PMID: 30654183 DOI: 10.1016/j.nano.2018.12.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 12/16/2018] [Accepted: 12/26/2018] [Indexed: 02/07/2023]
Abstract
Detecting deep tumors inside living subject is still challenging for Cerenkov luminescence imaging (CLI). In this study, a high-sensitivity endoscopic CLI (ECLI) system was developed with a dual-mode deep cooling approach to improve the imaging sensitivity. System was characterized through a series of ex vivo studies. Furthermore, subcutaneous and orthotropic human hepatocellular carcinoma (HCC) mouse models were established for ECLI guided tumor resection in vivo. The results showed that the ECLI system had spatial resolution (62.5 μm) and imaging sensitivity (6.29 × 10-2 kBq/μl 18F-FDG). The in vivo experimental data from the HCC mouse models demonstrated that the system was effective to intraoperatively guide the surgery of deep tumors such as liver cancer. Overall, the developed system exhibits promising potential for the applications of tumor precise resection and novel nanoprobe based optical imaging.
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Affiliation(s)
- Zeyu Zhang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Meishan Cai
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Chengpeng Bao
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Jie Tian
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
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Hou Y, Wang C, Chen M, Wang M, Deng G, Yang H, Zhou Z, Yang S. Iridium complex nanoparticle mediated radiopharmaceutical-excited phosphorescence imaging. Chem Commun (Camb) 2019; 55:14442-14445. [DOI: 10.1039/c9cc07399j] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
We firstly perpared liposome coated [Ir(pq)2(bpy)]Cl (Ir@liposome) as a transducer for radiopharmaceutical (18F-FDG) excited phosphorescence imaging (REPI).
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Affiliation(s)
- Yutong Hou
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
| | - Chenchen Wang
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
| | - Ming Chen
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
| | - Mingwei Wang
- Department of Nuclear Medicine
- Shanghai Medical College
- Fudan University
- Shanghai 200032
- China
| | - Guang Deng
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
| | - Hong Yang
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
| | - Zhiguo Zhou
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
| | - Shiping Yang
- The Education Ministry Key Lab of Resource Chemistry and Shanghai Key Laboratory of Rare Earth Functional Materials, Shanghai Normal University
- Shanghai
- China
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An Y, Wang K, Tian J. Recent methodology advances in fluorescence molecular tomography. Vis Comput Ind Biomed Art 2018; 1:1. [PMID: 32240398 PMCID: PMC7098398 DOI: 10.1186/s42492-018-0001-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 06/30/2018] [Indexed: 12/26/2022] Open
Abstract
Molecular imaging (MI) is a novel imaging discipline that has been continuously developed in recent years. It combines biochemistry, multimodal imaging, biomathematics, bioinformatics, cell & molecular physiology, biophysics, and pharmacology, and it provides a new technology platform for the early diagnosis and quantitative analysis of diseases, treatment monitoring and evaluation, and the development of comprehensive physiology. Fluorescence Molecular Tomography (FMT) is a type of optical imaging modality in MI that captures the three-dimensional distribution of fluorescence within a biological tissue generated by a specific molecule of fluorescent material within a biological tissue. Compared with other optical molecular imaging methods, FMT has the characteristics of high sensitivity, low cost, and safety and reliability. It has become the research frontier and research hotspot of optical molecular imaging technology. This paper took an overview of the recent methodology advances in FMT, mainly focused on the photon propagation model of FMT based on the radiative transfer equation (RTE), and the reconstruction problem solution consist of forward problem and inverse problem. We introduce the detailed technologies utilized in reconstruction of FMT. Finally, the challenges in FMT were discussed. This survey aims at summarizing current research hotspots in methodology of FMT, from which future research may benefit.
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Affiliation(s)
- Yu An
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kun Wang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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