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Zhang Z, Du Y, Shi X, Wang K, Qu Q, Liang Q, Ma X, He K, Chi C, Tang J, Liu B, Ji J, Wang J, Dong J, Hu Z, Tian J. NIR-II light in clinical oncology: opportunities and challenges. Nat Rev Clin Oncol 2024; 21:449-467. [PMID: 38693335 DOI: 10.1038/s41571-024-00892-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/03/2024]
Abstract
Novel strategies utilizing light in the second near-infrared region (NIR-II; 900-1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis. Furthermore, the use of NIR-II light promises to improve cancer phototherapy by enabling the selective delivery of increased therapeutic energy to tissues at greater depths. Initial clinical studies of NIR-II-based imaging and phototherapy have indicated impressive potential to decrease cancer recurrence, reduce complications and prolong survival. Despite the encouraging results achieved, clinical translation of innovative NIR-II techniques remains challenging and inefficient; multidisciplinary cooperation is necessary to bridge the gap between preclinical research and clinical practice, and thus accelerate the translation of technical advances into clinical benefits. In this Review, we summarize the available clinical data on NIR-II-based imaging and phototherapy, demonstrating the feasibility and utility of integrating these technologies into the treatment of cancer. We also introduce emerging NIR-II-based approaches with substantial potential to further enhance patient outcomes, while also highlighting the challenges associated with imminent clinical studies of these modalities.
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Affiliation(s)
- Zeyu Zhang
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Qiaojun Qu
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Qian Liang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Kunshan He
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Chongwei Chi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Jianqiang Tang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Liu
- Department of General Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiafu Ji
- Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China.
| | - Jun Wang
- Thoracic Oncology Institute/Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
| | - Jie Tian
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China.
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.
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Zhao Y, Li S, Zhang L, Tang Z, Wei D, Zhang H, Xie Q, Yi H, He X. Two-step reconstruction framework of fluorescence molecular tomography based on energy statistical probability. JOURNAL OF BIOPHOTONICS 2024; 17:e202300480. [PMID: 38351740 DOI: 10.1002/jbio.202300480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/17/2024] [Accepted: 01/28/2024] [Indexed: 05/04/2024]
Abstract
Fluorescence molecular tomography (FMT), as a promising technique for early tumor detection, can non-invasively visualize the distribution of fluorescent marker probe three-dimensionally. However, FMT reconstruction is a severely ill-posed problem, which remains an obstacle to wider application of FMT. In this paper, a two-step reconstruction framework was proposed for FMT based on the energy statistical probability. First, the tissue structural information obtained from computed tomography (CT) is employed to associate the tissue optical parameters for rough solution in the global region. Then, according to the global-region reconstruction results, the probability that the target belongs to each region can be calculated. The region with the highest probability is delineated as region of interest to realize accurate and fast source reconstruction. Numerical simulations and in vivo experiments were carried out to evaluate the effectiveness of the proposed framework. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.
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Affiliation(s)
- 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
| | - Shuangchen 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
| | - Zijian Tang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - De Wei
- 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
| | - Qiong Xie
- The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Huangjian Yi
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Xiaowei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, China
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Hu Y, Wu Y, Li L, Gu L, Zhu X, Jiang J, Ren W. Simultaneous reconstruction of 3D fluorescence distribution and object surface using structured light illumination and dual-camera detection. OPTICS EXPRESS 2024; 32:15760-15773. [PMID: 38859218 DOI: 10.1364/oe.517189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/26/2024] [Indexed: 06/12/2024]
Abstract
Fluorescence molecular tomography (FMT) serves as a noninvasive modality for visualizing volumetric fluorescence distribution within biological tissues, thereby proving to be an invaluable imaging tool for preclinical animal studies. The conventional FMT relies upon a point-by-point raster scan strategy, enhancing the dataset for subsequent reconstruction but concurrently elongating the data acquisition process. The resultant diminished temporal resolution has persistently posed a bottleneck, constraining its utility in dynamic imaging studies. We introduce a novel system capable of simultaneous FMT and surface extraction, which is attributed to the implementation of a rapid line scanning approach and dual-camera detection. The system performance was characterized through phantom experiments, while the influence of scanning line density on reconstruction outcomes has been systematically investigated via both simulation and experiments. In a proof-of-concept study, our approach successfully captures a moving fluorescence bolus in three dimensions with an elevated frame rate of approximately 2.5 seconds per frame, employing an optimized scan interval of 5 mm. The notable enhancement in the spatio-temporal resolution of FMT holds the potential to broaden its applications in dynamic imaging tasks, such as surgical navigation.
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Salimi M, Roshanfar M, Tabatabaei N, Mosadegh B. Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine. J Pers Med 2023; 14:33. [PMID: 38248734 PMCID: PMC10817559 DOI: 10.3390/jpm14010033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/08/2023] [Accepted: 12/20/2023] [Indexed: 01/23/2024] Open
Abstract
Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.
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Affiliation(s)
| | - Majid Roshanfar
- Department of Mechanical Engineering, Concordia University, Montreal, QC H3G 1M8, Canada;
| | - Nima Tabatabaei
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada;
| | - Bobak Mosadegh
- Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
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Zhang X, Jia Y, Cui J, Zhang J, Cao X, Zhang L, Zhang G. Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2023; 40:1359-1371. [PMID: 37706737 DOI: 10.1364/josaa.489702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/23/2023] [Indexed: 09/15/2023]
Abstract
Fluorescence molecular tomography (FMT) is a preclinical optical tomographic imaging technique that can trace various physiological and pathological processes at the cellular or even molecular level. Reducing the number of FMT projection views can improve the data acquisition speed, which is significant in applications such as dynamic problems. However, a reduction in the number of projection views will dramatically aggravate the ill-posedness of the FMT inverse problem and lead to significant degradation of the reconstructed images. To deal with this problem, we have proposed a deep-learning-based reconstruction method for sparse-view FMT that only uses four perpendicular projection views and divides the image reconstruction into two stages: image restoration and inverse Radon transform. In the first stage, the projection views of the surface fluorescence are restored to eliminate the blur derived from photon diffusion through a fully convolutional neural network. In the second stage, another convolutional neural network is used to implement the inverse Radon transform between the restored projections from the first stage and the reconstructed transverse slices. Numerical simulation and phantom and mouse experiments are carried out. The results show that the proposed method can effectively deal with the image reconstruction problem of sparse-view FMT.
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