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

无创 4D 胸部成像基础设施，支持重症监护病房 (ICU) 肺部疾病管理决策。

• 批准号: RTI-2021-00595
• 负责人: Cheriet, Farida
• 金额: $ 10.93万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718853
• 关键词: Non; invasive; 4D; thoracic; imaging

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.

数字图像采集系统发展迅速，在医疗领域得到广泛应用。这些系统产生大量多维数据，特别是当几个成像系统一起使用时（即多模式医学成像）。一方面，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存在显著差距。