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HYPerspectral Enhanced Reality (HYPER): a physiology-based surgical guidance tool

机译：高光谱增强现实（超级）：基于生理学的外科手术工具

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Background HSI is an optical technology allowing for a real-time, contrast-free snapshot of physiological tissue properties, including oxygenation. Hyperspectral imaging (HSI) has the potential to quantify the gastrointestinal perfusion intraoperatively. This experimental study evaluates the accuracy of HSI, in order to quantify bowel perfusion, and to obtain a superposition of the hyperspectral information onto real-time images. Methods In 6 pigs, 4 ischemic bowel loops were created (A, B, C, D) and imaged at set time points (from 5 to 360 min). A commercially available HSI system provided pseudo-color maps of the perfusion status (StO2, Near-InfraRed perfusion) and the tissue water index. An ad hoc software was developed to superimpose HSI information onto the live video, creating the HYPerspectral-based Enhanced Reality (HYPER). Seven regions of interest (ROIs) were identified in each bowel loop according to StO2 ranges, i.e., vascular (VASC proximal and distal), marginal vascular (MV proximal and distal), marginal ischemic (MI proximal and distal), and ischemic (ISCH). Local capillary lactates (LCL), reactive oxygen species (ROS), and histopathology were measured at the ROIs. A machine-learning-based prediction algorithm of LCL, based on the HSI-StO2%, was trained in the 6 pigs and tested on 5 additional animals. Results HSI parameters (StO2 and NIR) were congruent with LCL levels, ROS production, and histopathology damage scores at the ROIs discriminated by HYPER. The global mean error of LCL prediction was 1.18 +/- 1.35 mmol/L. For StO2 values > 30%, the mean error was 0.3 +/- 0.33. Conclusions HYPER imaging could precisely quantify the overtime perfusion changes in this bowel ischemia model.
机译：背景技术HSI是一种允许实时的生理组织特性的实时无畸形快照，包括氧合。高光谱成像（HSI）具有术中量化胃肠灌注。该实验研究评估了HSI的准确性，以便量化肠灌注，并在实时图像上获得高光谱信息的叠加。方法在6只猪中，在设定时间点（5至360分钟）时形成4个缺血性肠环（A，B，C，D）和成像。市售的HSI系统提供了灌注状态的伪彩色图（STO2，近红外灌注）和组织水指数。开发了一个ad hoc软件以将HSI信息叠加到实时视频，从而创建基于高光谱的增强现实（超级）。根据STO2范围，即血管（VASC近端和远端），边缘血管（MV近端和远端），边缘缺血（MI近端和远端），缺血（MI近端和远端），缺血（MI近端和远端）和缺血（ISCH）中的七个感兴趣区域（ROI）。和缺血（ISCH ）。在ROI上测量局部毛细血管乳酸盐（LCL），反应性氧物质（ROS）和组织病理学。基于HSI-STO2％的LCL基于机器学习的预测算法，在6只猪中培训并在5只动物上进行测试。结果HSI参数（STO2和NIR）与LCL水平，ROS生产和组织病理学损害分数一致，由超级歧视的ROI。 LCL预测的全局平均误差为1.18 +/- 1.35 mmol / L.对于STO2值> 30％，平均误差为0.3 +/- 0.33。结论超成像可以精确定量该肠缺血模型的加班灌注变化。

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