Non-invasive 4D thoracic imaging infrastructure to support decision-making in the management of lung diseases in intensive care units (ICU).

数字图像采集系统已经迅速发展并且在医学领域中变得广泛。这些系统生成大量的多维数据，特别是当几个成像系统一起使用时（即，多模态医学成像）。这在来自这些系统的数据集的大小与允许临床用户提取和可视化最相关信息的软件工具的可用性之间产生了显著的差距。 在这种情况下，需要新的工具来更好地显示多模态图像，以减少用户的心理负担。因此，所要求的设备将支持的研究计划的目标是开发和验证非侵入性4D（三维空间+时间）成像系统，以真实的时间查看胸腔的所有区域。本研究的医学目的是帮助医生在危重患者呼吸衰竭之前检测肺功能的恶化。该系统的性能将在肺功能恶化的高风险患者身上进行评估，例如在肺部并发症常见的呼吸道疾病大流行中。 呼吸的目的是提供氧气和清除身体产生的二氧化碳。这种气体交换经常受损的患者入住重症监护室（ICU）时，他们的肺功能恶化，这可能会发生各种原因。目前，没有成像系统允许ICU中的临床医生连续地并且真实的实时地看到胸腔的不同区域是如何工作的。如果他们能得到这些信息，就能在病人的呼吸状况恶化之前发现即将出现的问题。视觉信息流还将指导医生进行诊断，并帮助他们管理呼吸衰竭病例，因为他们必须使用药物和其他治疗方法。 为此，我们将开发一个多模式平台，该平台将合并来自三个不同系统的信息：1）距离传感（RGB-D）相机，2）红外相机，3）电阻抗断层扫描（EIT）设备。RGB-D摄像机（我们已经拥有）将使用胸部表面的4D重建来监测胸腔容积随时间的变化。红外摄像机可以测量精确的温度变化，并与RGB-D摄像机相结合，将使我们能够“看到毯子下”，更精确地测量病人的胸腔容积。EIT是一种无辐射的医疗设备，可以连续监测肺部的通气分布。它将提供有关肺容量的动态信息。 所要求的设备（EIT和红外摄像机）对我们的研究至关重要，并将导致多模态图像计算建模的知识进步和创新的可视化工具，在用户与复杂数据集的交互过程中为用户提供即时反馈。

Digital image acquisition systems have evolved rapidly and become widespread in the medical field. These systems generate large amounts of multi-dimensional data, particularly when several imaging systems are used together (i.e. multimodal medical imaging). This creates a significant gap between the size of the datasets coming from these systems, on one hand, and the availability of software tools that allow clinical users to extract and visualize the most relevant information, on the other hand. In that context, new tools are needed to better display multimodal images in order to reduce the mental load for the user. Therefore, the objective of the research programs that the requested equipment will support is to develop and validate a non-invasive 4D (three-dimensional space + time) imaging system to see all the areas of the chest cavity in real time. The medical aim of this research is to help physicians to detect the worsening of lung function prior to respiratory failure in critically ill patients. The system's performance will be evaluated on patients at high risk of lung function deterioration, such as in a respiratory disease pandemic where pulmonary complications are common. The purpose of breathing is to provide oxygen and remove the CO2 produced by the body. This gas exchange is frequently impaired in patients admitted to intensive care units (ICUs) when their lung function deteriorates, which can happen for various reasons. Currently, there is no imaging system allowing clinicians in the ICU to see how the different areas of the thoracic (chest) cavity are working, continuously and in real time. If they could have this information, it would allow them to detect oncoming problems, before the patient's breathing condition deteriorates. The flow of visual information would also guide doctors in their diagnosis and help them manage cases of respiratory failure, for which they must administer drugs and use other treatments. To that end, we will develop a multimodal platform that will merge the information from three different systems: 1) a range-sensing (RGB-D) camera, 2) an infra-red camera, and 3) an Electrical Impedance Tomography (EIT) device. The RGB-D camera (which we already have) will monitor the thoracic volume over time using 4D reconstruction of the chest surface. The infra-red camera can measure precise temperature changes and, combined with the RGB-D camera, will allow us to “see under the blanket" and more precisely measure the patient's thoracic volume. EIT is a radiation-free medical device that can continuously monitor the ventilation distribution in the lungs. It will provide dynamic information on the lung volumes. The requested equipment (EIT and infrared camera) is essential to conduct our research and will lead to knowledge advancements in computational modeling from multimodal images and innovative visualization tools providing immediate feedback to users during their interactions with complex datasets.