Vibroacoustic sensorics for medical device guidance

我们建议使用在医疗介入设备(如针、活组织检查系统、导管、夹持器、介入工具)与人体组织相互作用过程中产生的振动声信号来添加有关临床事件的有价值的信息，提供定向指导，从而减少相关的医疗错误。我们在该领域的先前工作已经表明，进入新组织的装置尖端(远端)在很大程度上负责在近端接收到的音频信号，并且进入不同的组织会改变音频特性。到目前为止，我们已经成功地使用连接到近端的初始传感器设计在实验室设置中获得数据点，从而消除了开发专用临床干预工具、电缆和减少无菌问题的需要。这项技术很可能在不对现有和经过验证的临床工具进行重大修改的情况下发挥作用。这项提议的研究工作将试图将这项实验室技术引入基于针头的特定临床应用(进入股血管-静脉和动脉)，从而可能导致提高手术质量。专用近端MEMS音频传感器将数据传输到采集系统，该采集系统对信号进行预处理，提取相关特征(高级信号处理)，并尝试(机器学习)描述介入期间的重要事件，例如跨越主组织层或进入和离开血管，并给出朝向目标目的地的方向信息和关于已达到医学目标的确认。我们将与临床工作人员密切互动，并记录实际过程、过程中的交互以及其他相关事件，作为系统建模的基础。理解和模拟信号及其行为的一个重要发展步骤是建立一个专用的合成体模，以模拟和匹配先前从进入动物组织样本中获得的结果。这与医疗程序信息一起被用来建立一个分类模型，该模型将允许我们基于诊断成像分析(超声)模拟人类干预。随后将使用在临床介入放射学设置中的正常导管操作期间获得的真实患者数据来测试、校正和优化该设置。下一步是在模体中验证拟议的指导方法，表明外科医生的表现(靶向准确性、手术速度、置信度)有所提高。这项研究在其他相邻和相连的研究领域具有巨大的潜力，可能会提高指导精度，基于音频信号对不同软组织进行表征，并为未来的(半)自主机器人手术/导管介入提供额外的指导信息和模拟的触觉反馈/事件信息。

We propose to use vibroacoustic signals that are generated during the interaction of medical interventional devices (e.g. needles, biopsy systems, catheters, graspers, interventional tools) with human tissue to add valuable information about clinical events, provide directional guidance and subsequently reduce related medical errors. Our prior work in that field has shown that the device tip (distal end) entering new tissue is to a large extent responsible for an audio signal received on the proximal end and that entering different tissues changes the audio characteristics. So far, we have managed to obtain data points in a laboratory setup using an initial sensor design attached to the proximal end and by that eliminating the need to develop dedicated clinical intervention tools, cables and reducing sterility issues. This technology likely will work without major alteration to existing and proven clinical tools. The research work of this proposal will attempt to introduce this laboratory technology to a specific needle-based clinical application (access to the femoral vessels - venous as well as arterial) that could lead to improve the quality of the procedure. A purpose build proximal MEMS audio sensor will transfer the data to an acquisition system that pre-processes the signal, extracts relevant features (advanced signal processing), and attempts to characterize (machine learning) important events during the intervention, like crossing main tissue layers or entering and leaving the vessel, and to give directional information towards the target destination and a confirmation on having reached the medical goal. We will closely interact with the clinical staff and record the actual procedure, interactions during the procedure, and other relevant events as a base for the system modeling. An important development step to understand and simulate signals and their behaviors is to build a dedicated synthetic phantom to model and match the findings of prior acquired results from entering animal tissue samples. This - together with the medical procedure information - is used to build a classification model that will allow us to simulate human interventions based on diagnostic imaging analysis (ultrasound). The setup will subsequently be tested, corrected, and optimized with real patient data obtained during a normal catheter procedure in a clinical interventional radiology setup. The next step is to validate the proposed guidance methodology in phantoms by indicating the increase in surgeonsperformance (targeting accuracy, speed of the procedure, confidence level. This research has enormous potential in other adjacent and connected research areas possibly allowing improved guidance accuracy, audio signal-based characterization of different soft tissues, as well as to provide additional guidance information and simulated haptic feedback/event information for a future (semi-)autonomous robotic surgery/catheter intervention.