Diversity Supplement: Enabling advanced endovascular beating-heart procedures through soft robotics

多样性补充：通过软机器人技术实现先进的血管内跳动心脏手术

• 批准号: 10508008
• 负责人: Tommaso Ranzani
• 金额: $ 10.84万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10508008
• 关键词: 3D ultrasound; Anatomy; Awareness; Biological; Biology; Boston; Breathing; Cardiac; Cardiac Surgery procedures; Catheters; Charge; Cilia; Clinical; Complement; Data; Distal; Educational workshop; Elements; Engineering; Ensure; Environment; Event; Family suidae; Feedback; Female; Goals; Hair; Health; Heart; Hispanic; Image; Image-Guided Surgery; In Vitro; Industrialization; Knowledge; Length; Liquid substance; Machine Learning; Mechanics; Mentors; Mentorship; Methods; Modality; Modeling; Monitor; Myocardial Contraction; Operative Surgical Procedures; Outcome; Perception; Performance; Persons; Positioning Attribute; Procedures; Program Development; Research; Risk; Robot; Robotics; Route; Safety; Sensory; Side; Slide; Structure; Students; Surgeon; Talents; Technology; Testing; Time; Tissues; Touch sensation; Tricuspid valve structure; Underrepresented Populations; Universities; Validation; arm; base; biomaterial compatibility; cantilever; career; clinically significant; cohort; community building; design; dexterity; doctoral student; experience; experimental study; force sensor; haptic feedback; improved; innovation; machine learning algorithm; machine learning method; meetings; miniaturize; miniaturized sensor; parent grant; pressure; pressure sensor; professor; recruit; robot control; sensor; sensory input; tool; transmission process; visual feedback

Project Summary/Abstract In beating heart procedures such as tricuspid annulus reduction, only visual feedback is provided to the surgeon via imaging and the accuracy in operating surgical tools relies on the quality of the intraoperative imaging. Hence, there is a need for safe, advanced methods to monitor position and interaction forces of the robot with the heart tissues to ensure safe and accurate manipulation of delicate heart structures. This proposal aims at developing an exteroceptive probe that detects pressures applied to the heart structures by the soft robot developed in the parent grant and contact forces within the heart. Such sensing capabilities will complement conventional imaging in increasing accuracy in positioning the robot inside the beating heart. It will also allow to monitor interaction forces and contacts with the heart anatomy. Machine learning (ML) methods will be investigated to discriminate and decouple the various raw sensory inputs and use them to identify tissues the probe is in contact with for better localization, safety, and to guide the procedure. The proposed research has two aims: Aim 3 focuses on sensor design and fabrication of the probe; Aim 4 handles sensor integration with the soft deployable robot, sensory interpretation based on ML, and function validation through in-vitro and ex-vivo testing. Combined with external and internal imaging, we hypothesize that the addition of this sensor will increase the accuracy and safety during beating heart surgical procedures. To monitor pressures applied distally by the robot a soft pressure sensor will be developed. Miniaturized soft contact sensors will also be integrated on the robot. These sensors will be inspired by the hair-like cilia observed in biology. Acting similarly to cantilever beams, cilia-like extensions can deflect via fluidic drag forces producing piezoelectric charges and generating a direction-dependent potential to sense flow rate and direction. We will develop cilia-inspired sensors around the pressure sensor to monitor contacts experienced by the robot. Taking advantage of the multiple sensing modalities on the exteroceptive prove, we will investigate the use of ML application to enhance soft robot awareness and provide tissue recognition and haptic feedback to the surgeon. These additional components will improve the robot perception of the surroundings. This will lead to 1) increased accuracy in positioning by integrating the information from onboard sensing with external imaging, 2) increase safety via monitoring contact and exchanged forces with surrounding heart structures and potentially paving the way towards integration of haptic feedback, and 3) further increase the accuracy in the control of the robot by exploiting sensor data to correct model estimation errors. 1

项目总结/摘要 在诸如三尖瓣环减小的跳动心脏手术中，仅向心脏起搏器提供视觉反馈。 外科医生通过成像和手术工具操作的准确性依赖于术中的质量 显像因此，需要安全、先进的方法来监测微结构的位置和相互作用力。 机器人与心脏组织，以确保安全和准确的操作微妙的心脏结构。这项建议 旨在开发一种外感受性探头，用于检测软机器人施加在心脏结构上的压力 在母体格兰特和心脏内的接触力中发展。这种传感能力将补充 传统成像在增加将机器人定位在跳动的心脏内的准确性方面的优势。它还将允许 监测与心脏解剖结构的相互作用力和接触。机器学习（ML）方法将是 研究了区分和解耦各种原始感觉输入，并使用它们来识别组织， 探头与接触，以更好地定位、安全并指导手术。 本论文的研究目标有两个：第三个目标是传感器的设计和探针的制作; 4处理传感器与软部署机器人的集成，基于ML的感官解释，以及功能 通过体外和离体测试进行验证。结合外部和内部成像，我们假设， 该传感器的增加将增加跳动心脏外科手术过程中的准确性和安全性。到 监测由机器人远端施加的压力，将开发软压力传感器。小型化软接触 传感器也将集成在机器人上。这些传感器的灵感来自于 生物学作用类似于悬臂梁，纤毛状延伸部可以通过流体拖曳力偏转， 压电充电并产生方向相关电势以感测流速和方向。我们将 在压力传感器周围开发受纤毛启发的传感器，以监测机器人经历的接触。以 的优势，多种感知方式的外感受性证明，我们将研究使用ML 应用程序，以增强软机器人的意识，并提供组织识别和触觉反馈给外科医生。 这些额外的组件将改善机器人对周围环境的感知。这将导致1） 通过将来自机载传感的信息与外部成像相结合，提高了定位精度，2） 通过监测与周围心脏结构的接触和交换力来增加安全性， 为触觉反馈的集成铺平了道路，以及3）进一步提高了控制的准确性。 机器人通过利用传感器数据来校正模型估计误差。 1

项目成果

A Collapsible Soft Actuator Facilitates Performance in Constrained Environments.

可折叠软执行器可提高受限环境中的性能。

• DOI: 10.1002/aisy.202200085
• 发表时间: 2022
• 期刊: Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)
• 作者: Rogatinsky,Jacob;Gomatam,Kiran;Lim,ZiHeng;Lee,Megan;Kinnicutt,Lorenzo;Duriez,Christian;Thomson,Perry;McDonald,Kevin;Ranzani,Tommaso
• 通讯作者: Ranzani,Tommaso

Magnetically induced stiffening for soft robotics.

软机器人的磁感应硬化。

• DOI: 10.1039/d2sm01390h
• 发表时间: 2023
• 期刊: Soft matter
• 影响因子: 3.4
• 作者: Gaeta,LeahT;McDonald,KevinJ;Kinnicutt,Lorenzo;Le,Megan;Wilkinson-Flicker,Sidney;Jiang,Yixiao;Atakuru,Taylan;Samur,Evren;Ranzani,Tommaso
• 通讯作者: Ranzani,Tommaso