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Takamatsu T, Fukushima R, Sato K, Umezawa M, Yokota H, Soga K, Hernandez-Guedes A, Callico GM, Takemura H. Development of a visible to 1600 nm hyperspectral imaging rigid-scope system using supercontinuum light and an acousto-optic tunable filter. OPTICS EXPRESS 2024; 32:16090-16102. [PMID: 38859246 DOI: 10.1364/oe.515747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/22/2024] [Indexed: 06/12/2024]
Abstract
In this study, we developed a rigid-scope system that can perform hyperspectral imaging (HSI) between visible and 1600 nm wavelengths using a supercontinuum light source and an acousto-optic tunable filter to emit specific wavelengths. The system optical performance was verified, and the classification ability was investigated. Consequently, it was demonstrated that HSI (490-1600 nm) could be performed. In addition, seven different targets could be classified by the neural network with an accuracy of 99.6%, recall of 93.7%, and specificity of 99.1% when the wavelength range of over 1000 nm (OTN) was extracted from HSI data as train data.
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Mori A, Umezawa M, Okubo K, Kamiya T, Kamimura M, Ohtani N, Soga K. Visualization of hydrocarbon chain length and degree of saturation of fatty acids in mouse livers by combining near-infrared hyperspectral imaging and machine learning. Sci Rep 2023; 13:20555. [PMID: 37996472 PMCID: PMC10667523 DOI: 10.1038/s41598-023-47565-z] [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: 06/13/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023] Open
Abstract
Fatty acids play various physiological roles owing to their diverse structural characteristics, such as hydrocarbon chain length (HCL) and degree of saturation (DS). Although the distribution of fatty acids in biological tissues is associated with lipid metabolism, in situ imaging tools are still lacking for HCL and DS. Here, we introduce a framework of near-infrared (1000-1400 nm) hyperspectral label-free imaging with machine learning analysis of the fatty acid HCL and DS distribution in the liver at each pixel, in addition to the previously reported total lipid content. The training data of 16 typical fatty acids were obtained by gas chromatography from liver samples of mice fed with various diets. A two-dimensional mapping of these two parameters was successfully performed. Furthermore, the HCL/DS plot exhibited characteristic clustering among the different diet groups. Visualization of fatty acid distribution would provide insights for revealing the pathophysiological conditions of liver diseases and metabolism.
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Affiliation(s)
- Akino Mori
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Masakazu Umezawa
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan.
| | - Kyohei Okubo
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Tomonori Kamiya
- Department of Pathophysiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Masao Kamimura
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan
| | - Naoko Ohtani
- Department of Pathophysiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Kohei Soga
- Department of Materials Science and Technology, Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika, Tokyo, 125-8585, Japan.
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Waterhouse DJ, Privitera L, Anderson J, Stoyanov D, Giuliani S. Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:094804. [PMID: 36993142 PMCID: PMC10042297 DOI: 10.1117/1.jbo.28.9.094804] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics. AIM Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization. APPROACH A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts ( n = 6 ) after the injection of a NB-specific NIR-I fluorescent probe (Dinutuximab-IRDye800). We constructed image cubes representing fluorescence collected from ∼ 850 to 1450 nm and compared the performance of seven learning-based methods for pixel-by-pixel classification, including linear discriminant analysis, k -nearest neighbor classification, and a neural network. RESULTS The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and k -nearest-neighbor approach with area under curve normalization performed best, achieving 97.5% per-pixel classification accuracy (97.1%, 93.5%, and 99.2% for tumor, non-tumor tissue and background, respectively). CONCLUSIONS The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS.
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Affiliation(s)
- Dale J. Waterhouse
- University College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
| | - Laura Privitera
- University College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, Cancer Section, Developmental Biology and Cancer Programme, London, United Kingdom
| | - John Anderson
- UCL Great Ormond Street Institute of Child Health, Cancer Section, Developmental Biology and Cancer Programme, London, United Kingdom
| | - Danail Stoyanov
- University College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
| | - Stefano Giuliani
- University College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom
- UCL Great Ormond Street Institute of Child Health, Cancer Section, Developmental Biology and Cancer Programme, London, United Kingdom
- Great Ormond Street Hospital for Children NHS Trust, Department of Specialist Neonatal and Paediatric Surgery, London, United Kingdom
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Rienda I, Moro E, Pérez-Rubio Á, Trullenque-Juan R, Pérez-Guaita D, Lendl B, Kuligowski J, Castell JV, Pérez-Rojas J, Pareja E, Quintás G. Comparing the direct assessment of steatosis in liver explants with mid- and near-infrared vibrational spectroscopy, prior to organ transplantation. Analyst 2023; 148:3986-3991. [PMID: 37539806 DOI: 10.1039/d3an01184d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
A fast and accurate assessment of liver steatosis is crucial during liver transplantation surgery as it can negatively impact its success. Recent research has shown that near-infrared (NIR) and attenuated total reflectance-Fourier transform mid-infrared (ATR-FTIR) spectroscopy could be used as real-time quantitative tools to assess steatosis during abdominal surgery. Here, in the frame of a clinical study, we explore the performance of NIR and ATR-FTIR spectroscopy for the direct assessment of steatosis in liver tissues. Results show that both NIR and ATR-FTIR spectroscopy are able to quantify the % of steatosis with cross-validation errors of 1.4 and 1.6%, respectively. Furthermore, the two portable instruments used both provided results within seconds and can be placed inside an operating room evidencing the potential of IR spectroscopy for initial characterization of grafts in liver transplantation surgery. We also evaluated the complementarity of the spectral ranges through correlation spectroscopy.
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Affiliation(s)
- Iván Rienda
- Pathology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Erika Moro
- Dept. Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain
| | - Álvaro Pérez-Rubio
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr Peset, Valencia, Spain
| | - Ramón Trullenque-Juan
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr Peset, Valencia, Spain
| | | | - Bernhard Lendl
- Institute of Chemical Technologies and Analytics, Vienna University of Technology, Vienna, Austria
| | - Julia Kuligowski
- Neonatal Research Group, Health Research Institute La Fe, Valencia, Spain
| | - Jose V Castell
- Dept. Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain
- Unit for Experimental Hepatology, Health Research Institute La Fe, Valencia, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Judith Pérez-Rojas
- Pathology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Eugenia Pareja
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr Peset, Valencia, Spain
- Unit for Experimental Hepatology, Health Research Institute La Fe, Valencia, Spain.
| | - Guillermo Quintás
- Health and Biomedicine, Leitat Technological Center, Valencia, Spain.
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Mitsui T, Mori A, Takamatsu T, Kadota T, Sato K, Fukushima R, Okubo K, Umezawa M, Takemura H, Yokota H, Kuwata T, Kinoshita T, Ikematsu H, Yano T, Maeda S, Soga K. Evaluating the identification of the extent of gastric cancer by over-1000 nm near-infrared hyperspectral imaging using surgical specimens. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:086001. [PMID: 37614567 PMCID: PMC10442660 DOI: 10.1117/1.jbo.28.8.086001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/22/2023] [Accepted: 08/02/2023] [Indexed: 08/25/2023]
Abstract
Significance Determining the extent of gastric cancer (GC) is necessary for evaluating the gastrectomy margin for GC. Additionally, determining the extent of the GC that is not exposed to the mucosal surface remains difficult. However, near-infrared (NIR) can penetrate mucosal tissues highly efficiently. Aim We investigated the ability of near-infrared hyperspectral imaging (NIR-HSI) to identify GC areas, including exposed and unexposed using surgical specimens, and explored the identifiable characteristics of the GC. Approach Our study examined 10 patients with diagnosed GC who underwent surgery between 2020 and 2021. Specimen images were captured using NIR-HSI. For the specimens, the exposed area was defined as an area wherein the cancer was exposed on the surface, the unexposed area as an area wherein the cancer was present although the surface was covered by normal tissue, and the normal area as an area wherein the cancer was absent. We estimated the GC (including the exposed and unexposed areas) and normal areas using a support vector machine, which is a machine-learning method for classification. The prediction accuracy of the GC region in every area and normal region was evaluated. Additionally, the tumor thicknesses of the GC were pathologically measured, and their differences in identifiable and unidentifiable areas were compared using NIR-HSI. Results The average prediction accuracy of the GC regions combined with both areas was 77.2%; with exposed and unexposed areas was 79.7% and 68.5%, respectively; and with normal regions was 79.7%. Additionally, the areas identified as cancerous had a tumor thickness of > 2 mm . Conclusions NIR-HSI identified the GC regions with high rates. As a feature, the exposed and unexposed areas with tumor thicknesses of > 2 mm were identified using NIR-HSI.
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Affiliation(s)
- Tomohiro Mitsui
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
- Yokohama City University Graduate School of Medicine, Department of Gastroenterology, Yokohama, Japan
| | - Akino Mori
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Toshihiro Takamatsu
- National Cancer Center, Exploratory Oncology Research and Clinical Trial Center, Chiba, Japan
- Tokyo University of Science, Research Institute for Biomedical Sciences, Chiba, Japan
| | - Tomohiro Kadota
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
| | - Konosuke Sato
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Ryodai Fukushima
- Tokyo University of Science, Department of Mechanical Engineering, Chiba, Japan
| | - Kyohei Okubo
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Masakazu Umezawa
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
| | - Hiroshi Takemura
- Tokyo University of Science, Research Institute for Biomedical Sciences, Chiba, Japan
- Tokyo University of Science, Department of Mechanical Engineering, Chiba, Japan
| | - Hideo Yokota
- RIKEN Center for Advanced Photonics, Saitama, Japan
| | - Takeshi Kuwata
- National Cancer Center Hospital East, Department of Pathology and Clinical Laboratories, Kashiwa, Japan
| | - Takahiro Kinoshita
- National Cancer Center Hospital East, Department of Gastric Surgery, Chiba, Japan
| | - Hiroaki Ikematsu
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
- National Cancer Center, Exploratory Oncology Research and Clinical Trial Center, Chiba, Japan
| | - Tomonori Yano
- National Cancer Center Hospital East, Department of Gastroenterology and Endoscopy, Kashiwa, Japan
| | - Shin Maeda
- Yokohama City University Graduate School of Medicine, Department of Gastroenterology, Yokohama, Japan
| | - Kohei Soga
- Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan
- Tokyo University of Science, Research Institute for Biomedical Sciences, Chiba, Japan
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Shang H, Shang L, Wu J, Xu Z, Zhou S, Wang Z, Wang H, Yin J. NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:121990. [PMID: 36327802 DOI: 10.1016/j.saa.2022.121990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.
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Affiliation(s)
- Hui Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Linwei Shang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Jinjin Wu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zhibing Xu
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Suwei Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zihan Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Huijie Wang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
| | - Jianhua Yin
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
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Hyperspectral Imaging for Viability Assessment of Human Liver Allografts During Normothermic Machine Perfusion. Transplant Direct 2022; 8:e1420. [PMID: 36406899 PMCID: PMC9671746 DOI: 10.1097/txd.0000000000001420] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 01/24/2023] Open
Abstract
UNLABELLED Normothermic machine perfusion (NMP) is nowadays frequently utilized in liver transplantation. Despite commonly accepted viability assessment criteria, such as perfusate lactate and perfusate pH, there is a lack of predictive organ evaluation strategies to ensure graft viability. Hyperspectral imaging (HSI)-as an optical imaging modality increasingly applied in the biomedical field-might provide additional useful data regarding allograft viability and performance of liver grafts during NMP. METHODS Twenty-five deceased donor liver allografts were included in the study. During NMP, graft viability was assessed conventionally and by means of HSI. Images of liver parenchyma were acquired at 1, 2, and 4 h of NMP, and subsequently analyzed using a specialized HSI acquisition software to compute oxygen saturation, tissue hemoglobin index, near-infrared perfusion index, and tissue water index. To analyze the association between HSI parameters and perfusate lactate as well as perfusate pH, we performed simple linear regression analysis. RESULTS Perfusate lactate at 1, 2, and 4 h NMP was 1.5 [0.3-8.1], 0.9 [0.3-2.8], and 0.9 [0.1-2.2] mmol/L. Perfusate pH at 1, 2, and 4 h NMP was 7.329 [7.013-7.510], 7.318 [7.081-7.472], and 7.265 [6.967-7.462], respectively. Oxygen saturation predicted perfusate lactate at 1 and 2 h NMP (R2 = 0.1577, P = 0.0493; R2 = 0.1831, P = 0.0329; respectively). Tissue hemoglobin index predicted perfusate lactate at 1, 2, and 4 h NMP (R2 = 0.1916, P = 0.0286; R2 = 0.2900, P = 0.0055; R2 = 0.2453, P = 0.0139; respectively). CONCLUSIONS HSI may serve as a noninvasive tool for viability assessment during NMP. Further evaluation and validation of HSI parameters are warranted in larger sample sizes.
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Guo H, Tikhomirov AB, Mitchell A, Alwayn IPJ, Zeng H, Hewitt KC. Real-time assessment of liver fat content using a filter-based Raman system operating under ambient light through lock-in amplification. BIOMEDICAL OPTICS EXPRESS 2022; 13:5231-5245. [PMID: 36425639 PMCID: PMC9664892 DOI: 10.1364/boe.467849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/23/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
During liver procurement, surgeons mostly rely on their subjective visual inspection of the liver to assess the degree of fatty infiltration, for which misclassification is common. We developed a Raman system, which consists of a 1064 nm laser, a handheld probe, optical filters, photodiodes, and a lock-in amplifier for real-time assessment of liver fat contents. The system performs consistently in normal and strong ambient light, and the excitation incident light penetrates at least 1 mm into duck fat phantoms and duck liver samples. The signal intensity is linearly correlated with MRI-calibrated fat contents of the phantoms and the liver samples.
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Affiliation(s)
- Hao Guo
- Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Road, Halifax, NS B3H 4R2, Canada
- Department of Medical Physics, Nova Scotia Health Authority, 5820 University Avenue Halifax, NS B3H 1V7, Canada
| | - Alexey B. Tikhomirov
- Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Road, Halifax, NS B3H 4R2, Canada
| | - Alexandria Mitchell
- Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Road, Halifax, NS B3H 4R2, Canada
- Department of Medical Physics, Nova Scotia Health Authority, 5820 University Avenue Halifax, NS B3H 1V7, Canada
| | - Ian Patrick Joseph Alwayn
- Department of Surgery, Leiden University Medical Center (LUMC) Transplant Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Haishan Zeng
- Imaging Unit, Integrative Oncology Department, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Kevin C. Hewitt
- Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Road, Halifax, NS B3H 4R2, Canada
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Browning CM, Mayes S, Mayes SA, Rich TC, Leavesley SJ. Microscopy is better in color: development of a streamlined spectral light path for real-time multiplex fluorescence microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:3751-3772. [PMID: 35991911 PMCID: PMC9352297 DOI: 10.1364/boe.453657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Spectroscopic image data has provided molecular discrimination for numerous fields including: remote sensing, food safety and biomedical imaging. Despite the various technologies for acquiring spectral data, there remains a trade-off when acquiring data. Typically, spectral imaging either requires long acquisition times to collect an image stack with high spectral specificity or acquisition times are shortened at the expense of fewer spectral bands or reduced spatial sampling. Hence, new spectral imaging microscope platforms are needed to help mitigate these limitations. Fluorescence excitation-scanning spectral imaging is one such new technology, which allows more of the emitted signal to be detected than comparable emission-scanning spectral imaging systems. Here, we have developed a new optical geometry that provides spectral illumination for use in excitation-scanning spectral imaging microscope systems. This was accomplished using a wavelength-specific LED array to acquire spectral image data. Feasibility of the LED-based spectral illuminator was evaluated through simulation and benchtop testing and assessment of imaging performance when integrated with a widefield fluorescence microscope. Ray tracing simulations (TracePro) were used to determine optimal optical component selection and geometry. Spectral imaging feasibility was evaluated using a series of 6-label fluorescent slides. The LED-based system response was compared to a previously tested thin-film tunable filter (TFTF)-based system. Spectral unmixing successfully discriminated all fluorescent components in spectral image data acquired from both the LED and TFTF systems. Therefore, the LED-based spectral illuminator provided spectral image data sets with comparable information content so as to allow identification of each fluorescent component. These results provide proof-of-principle demonstration of the ability to combine output from many discrete wavelength LED sources using a double-mirror (Cassegrain style) optical configuration that can be further modified to allow for high speed, video-rate spectral image acquisition. Real-time spectral fluorescence microscopy would allow monitoring of rapid cell signaling processes (i.e., Ca2+ and other second messenger signaling) and has potential to be translated to clinical imaging platforms.
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Affiliation(s)
- Craig M. Browning
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- Systems Engineering, University of South Alabama, AL 36688, USA
- These authors contributed equally to this work
| | - Samantha Mayes
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- These authors contributed equally to this work
| | - Samuel A. Mayes
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- Systems Engineering, University of South Alabama, AL 36688, USA
| | - Thomas C. Rich
- Pharmacology, University of South Alabama, AL 36688, USA
- Center for Lung Biology, University of South Alabama, AL 36688, USA
| | - Silas J. Leavesley
- Chemical and Biomolecular Engineering, University of South Alabama, AL 36688, USA
- Pharmacology, University of South Alabama, AL 36688, USA
- Center for Lung Biology, University of South Alabama, AL 36688, USA
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Wagner T, Katou S, Wahl P, Vogt F, Kneifel F, Morgul H, Vogel T, Houben P, Becker F, Struecker B, Pascher A, Radunz S. Hyperspectral imaging for quantitative assessment of hepatic steatosis in human liver allografts. Clin Transplant 2022; 36:e14736. [PMID: 35622345 DOI: 10.1111/ctr.14736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/25/2022] [Accepted: 05/01/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION In liver transplantation (LT), steatosis is commonly judged to be a risk factor for graft dysfunction, and quantitative assessment of hepatic steatosis remains crucial. Liver biopsy as the gold standard for evaluation of hepatic steatosis has certain drawbacks, i.e. invasiveness, and intra- and inter-observer variability. A non-invasive, quantitative modality could replace liver biopsy and eliminate these disadvantages, but has not yet been evaluated in human LT. METHODS We performed a pilot study to evaluate the feasibility and accuracy of hyperspectral imaging (HSI) in the assessment of hepatic steatosis of human liver allografts for transplantation. Thirteen deceased donor liver allografts were included in the study. The degree of steatosis was assessed by means of conventional liver biopsy as well as HSI, performed at the end of backtable preparation, during normothermic machine perfusion (NMP), and after reperfusion in the recipient. RESULTS Organ donors were 51 [30-83] years old, and 61.5% were male. Donor body mass index was 24.2 [16.5-38.0] kg/m2. The tissue lipid index (TLI) generated by HSI at the end of back-table preparation correlated significantly with the histopathologically assessed degree of overall hepatic steatosis (R2 = 0.9085, p<0.0001); this was based on a correlation of TLI and microvesicular steatosis (R2 = 0.8120; p<0.0001). There is also a linear relationship between the histopathologically assessed degree of overall steatosis and TLI during NMP (R2 = 0.5646; p = 0.0031) as well as TLI after reperfusion (R2 = 0.6562; p = 0.0008). CONCLUSION HSI may safely be applied for accurate assessment of hepatic steatosis in human liver grafts. Certainly, TLI needs further assessment and validation in larger sample sizes. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Tristan Wagner
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Shadi Katou
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Philip Wahl
- Diaspective Vision GmbH, Am Salzhaff, Germany
| | - Franziska Vogt
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Felicia Kneifel
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Haluk Morgul
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Thomas Vogel
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Philipp Houben
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Felix Becker
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Benjamin Struecker
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Andreas Pascher
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
| | - Sonia Radunz
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Münster, Germany
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Takamatsu T, Kitagawa Y, Akimoto K, Iwanami R, Endo Y, Takashima K, Okubo K, Umezawa M, Kuwata T, Sato D, Kadota T, Mitsui T, Ikematsu H, Yokota H, Soga K, Takemura H. Over 1000 nm Near-Infrared Multispectral Imaging System for Laparoscopic In Vivo Imaging. SENSORS 2021; 21:s21082649. [PMID: 33918935 PMCID: PMC8069262 DOI: 10.3390/s21082649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/01/2021] [Accepted: 04/06/2021] [Indexed: 01/17/2023]
Abstract
In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.
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Affiliation(s)
- Toshihiro Takamatsu
- Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba 277-8577, Japan;
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.S.); (H.T.)
- Correspondence: ; Tel.: +81-04-7133-1111
| | - Yuichi Kitagawa
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Kohei Akimoto
- Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.A.); (Y.E.)
| | - Ren Iwanami
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Yuto Endo
- Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.A.); (Y.E.)
| | - Kenji Takashima
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Kyohei Okubo
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Masakazu Umezawa
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Takeshi Kuwata
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan;
| | - Daiki Sato
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Tomohiro Kadota
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Tomohiro Mitsui
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Hiroaki Ikematsu
- Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Chiba 277-8577, Japan;
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Chiba 277-8577, Japan; (K.T.); (D.S.); (T.K.); (T.M.)
| | - Hideo Yokota
- RIKEN Center for Advanced Photonics, Wako, Saitama 351-0198, Japan;
| | - Kohei Soga
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.S.); (H.T.)
- Department of Materials Science and Technology, Tokyo University of Science, Katsushika, Tokyo 162-8601, Japan; (Y.K.); (R.I.); (K.O.); (M.U.)
| | - Hiroshi Takemura
- Research Institute for Biomedical Sciences, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.S.); (H.T.)
- Department of Mechanical Engineering, Tokyo University of Science, Noda, Chiba 278-8510, Japan; (K.A.); (Y.E.)
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