A near-infrared hyperspectral imaging system for quantitative monitoring of hemodynamics and metabolism on the exposed cortex of mice

被引:0
|
作者
Giannoni, Luca [1 ]
Lange, Frederic [1 ]
Tachtsidis, Ilias [1 ]
机构
[1] UCL, Dept Med Phys & Biomed Engn, Malet Pl Engn Bldg,Gower St, London WC1E 6BT, England
基金
英国惠康基金; 欧盟地平线“2020”;
关键词
Hyperspectral imaging; near-infrared spectroscopy; diffuse optical imaging; brain metabolism; cytochrome-c-oxidase; brain oxygenation; brain hemodynamics; TISSUE;
D O I
10.1117/12.2526599
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A near-infrared (NIR) hyperspectral imaging (HSI) system has been developed to measure the hemodynamic (changes in concentration of oxyhemoglobin and deoxyhemoglobin) and the metabolic (changes in concentration of oxidised cytochrome-c-oxidase) responses in the exposed cortex of small animals. Using the extended spectral information of multiple wavelengths in the NIR range between 780 and 900 nm optimal differentiation between the optical signatures of the chromophores (hemoglobin and cytochrome-c-oxidase) can be achieved. The system, called hNIR, is composed of: (1) a high-frame rate, large-format scientific CMOS (sCMOS) camera for image acquisition and (2) a broadband source coupled with a Pellin-Broca prism mounted on a rotating motor for sequential, fast-rate illumination of the target at different spectral bands. The system characterisation highlights the capability of the setup to achieve high spatial resolution over a similar to 1x1 mm field of view (FOV). Hyperspectral data analysis also includes simulations using a Monte Carlo optical model of HSI, to estimate the average photon pathlength and improve image reconstruction and quantification. The hNIR system described here is an improvement over a previously tested commercial snapshot HSI solution both in terms of spatial resolution and signal-to-noise ratio (SNR). This setup will be used to monitor brain hemodynamic and metabolic changes in the exposed cortex of mice during systemic oxygenation changes.
引用
收藏
页数:3
相关论文
共 50 条
  • [21] Fungal detection in wheat using near-infrared hyperspectral imaging
    Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada
    不详
    不详
    Trans. ASABE, 2007, 6 (2171-2176):
  • [22] Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging
    Workhwa, Saranya
    Khanthong, Thitirat
    Manmak, Napatsorn
    Thompson, Anthony Keith
    Teerachaichayut, Sontisuk
    HORTICULTURAE, 2024, 10 (04)
  • [23] Classification of pulse flours using near-infrared hyperspectral imaging
    Sivakumar, Chitra
    Chaudhry, Muhammad Mudassir Arif
    Paliwal, Jitendra
    LWT-FOOD SCIENCE AND TECHNOLOGY, 2022, 154
  • [24] Quality Assessment of Crop Seeds by Near-Infrared Hyperspectral Imaging
    Zhu, Dazhou
    Wang, Kun
    Zhang, Dongyan
    Huang, Wenjiang
    Yang, Guijun
    Ma, Zhihong
    Wang, Cheng
    SENSOR LETTERS, 2011, 9 (03) : 1144 - 1150
  • [25] Near-infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour
    Wang, Xiaobin
    Zhao, Chunjiang
    Huang, Wenqian
    Wang, Qingyan
    Liu, Chen
    Yang, Guiyan
    JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 2018, 98 (07) : 2793 - 2800
  • [26] Near-infrared hyperspectral imaging for classification of mung bean seeds
    Phuangsombut, Kaewkarn
    Ma, Te
    Inagaki, Tetsuya
    Tsuchikawa, Satoru
    Terdwongworakul, Anupun
    INTERNATIONAL JOURNAL OF FOOD PROPERTIES, 2018, 21 (01) : 799 - 807
  • [27] Hyperspectral near-infrared imaging for the detection of physical damages of pear
    Lee, Wang-Hee
    Kim, Moon S.
    Lee, Hoonsoo
    Delwiche, Stephen R.
    Bae, Hanhong
    Kim, Dae-Yong
    Cho, Byoung-Kwan
    JOURNAL OF FOOD ENGINEERING, 2014, 130 : 1 - 7
  • [28] Near-infrared hyperspectral imaging for polymer particle size estimation
    Pieszczek, Lukasz
    Daszykowski, Michal
    MEASUREMENT, 2021, 186
  • [29] Hyperspectral Near-infrared Reflectance Imaging for Detection of Defect Tomatoes
    Lee, Hoonsoo
    Kim, Moon S.
    Jeong, Danhee
    Chao, Kuanglin
    Cho, Byoung-Kwan
    Delwiche, Stephen R.
    SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY III, 2011, 8027
  • [30] Visible/near-infrared hyperspectral imaging for beef tenderness prediction
    Naganathan, Govindarajan Konda
    Grimes, Lauren M.
    Subbiah, Jeyamkondan
    Calkins, Chris R.
    Samal, Ashok
    Meyer, George E.
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2008, 64 (02) : 225 - 233