CPS: Synergy: Collaborative Research: MRI Powered & Guided Tetherless Effectors for Localized Therapeutic Interventions

CPS：协同作用：协作研究：MRI 驱动

• 批准号: 1646586
• 负责人: Dipan Shah
• 金额: $ 23.78万
• 依托单位: The Methodist Hospital Research Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646586&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; MRI

Magnetic Resonance Imaging (MRI) scanners use strong magnetic fields to safely image soft tissues deep inside the body. They offer a unique tool for guiding therapies: images while patient is inside the scanner can localize diseased tissue and guide an intervention with high accuracy. This research controls MRI magnetic fields to wirelessly push millimeter-scale robots through vessels in the body, assemble them into tools, and provide targeted drug delivery or pierce tissue. This will directly impact healthcare, improving patient outcome by enabling unparalleled minimal invasiveness resulting in faster recovery, fewer side effects, and cost-effectiveness. This transformative toolset for multi-agent control will set the foundation for a wealth of medical therapies and surgical interventions. Using magnetic forces of clinical MRI scanners to steer miniature tetherless effectors through human bodies and combining with real-time imaging and operator immersion could transform the practice of minimally invasive interventions. This CPS will seamlessly integrate physical (scanner sensor/actuator, effectors, patient, operator) and cyber (world modeling, combined sensor and effector control, operator immersion). Work entails: (1) Portfolio of parametric effector designs that can be optimized to exploit the constraints of a given clinical procedure. (2) Toolbox of automatic controllers for MRI-based powering and steering of tetherless effectors in the body lumen, self-assembling them into tools, and precision therapy delivery or to pierce tissue. (3) Real-time MRI-based sensing of the physical world for imaging and tracking effectors and tissue. (4) Linked effector and MRI scanner control on-the-fly. (5) Visual/force-feedback human-robot interfacing. The work focuses on two effector classes: an MRI Gauss gun that stores magnetic potential energy released by a chain reaction when robots self-assemble, and an MRI pile-driver that converts kinetic energy from an enclosed sphere into impulses to tunnel into tissue. These approaches will be validated through analytical modeling, scaled hardware experiments, and experiments in clinical MRI scanners.

磁共振成像（MRI）扫描仪使用强磁场安全地成像身体深处的软组织。它们为指导治疗提供了独特的工具：患者在扫描仪内时的图像可以定位病变组织，并以高精度指导干预。这项研究控制MRI磁场，以无线方式推动毫米级机器人通过体内血管，将它们组装成工具，并提供靶向药物输送或刺穿组织。这将直接影响医疗保健，通过实现无与伦比的微创来改善患者的预后，从而实现更快的恢复，更少的副作用和成本效益。这种用于多智能体控制的变革性工具集将为丰富的医学治疗和外科干预奠定基础。使用临床MRI扫描仪的磁力来引导微型无绳效应器通过人体，并结合实时成像和操作员沉浸，可以改变微创干预的实践。该CPS将无缝集成物理（扫描仪传感器/执行器、效应器、患者、操作员）和网络（世界建模、传感器和效应器组合控制、操作员沉浸）。工作包括：（1）可以优化的参数效应器设计的组合，以利用给定的临床程序的约束。(2)一系列自动控制器，用于在体腔内为无绳效应器提供基于MRI的动力和转向，将其自组装成工具，以及精确的治疗输送或刺穿组织。(3)基于MRI的物理世界实时感测，用于成像和跟踪效应器和组织。(4)联动效应器和MRI扫描仪实时控制。(5)视觉/力反馈人机接口。这项工作主要集中在两个效应器类别上：一个是MRI高斯枪，它存储机器人自组装时连锁反应释放的磁势能;另一个是MRI桩驱动器，它将封闭球体的动能转换为脉冲，并将其隧道化到组织中。这些方法将通过分析建模，缩放硬件实验，并在临床MRI扫描仪的实验进行验证。

项目成果

Manipulator-driven selection of semi-active MR-visible markers

机械手驱动的半主动 MR 可见标记选择

• DOI: 10.1002/rcs.1846
• 发表时间: 2018
• 期刊: The International Journal of Medical Robotics and Computer Assisted Surgery
• 作者: An, Junmo;Webb, Andrew G.;Shah, Dipan J.;Chin, Karen;Tsekos, Nikolaos V.
• 通讯作者: Tsekos, Nikolaos V.

Normal Reference Values and Reproducibility of Tricuspid Annulus Dimensions Using Cardiovascular Magnetic Resonance

• DOI: 10.1016/j.amjcard.2019.05.019
• 发表时间: 2019-08-15
• 期刊: AMERICAN JOURNAL OF CARDIOLOGY
• 影响因子: 2.8
• 作者: Zhan, Yang;Debs, Dany;Shah, Dipan J.
• 通讯作者: Shah, Dipan J.

Manipulation and control of microrobots using a novel permanent magnet stage

使用新型永磁平台操纵和控制微型机器人

• DOI: 10.1109/urai.2017.7992801
• 发表时间: 2017
• 期刊: 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI
• 作者: Sheckman, Samuel;Kim, Hoyeon;Manzoor, Sheryl;Rogowski, Louis W.;Huang, Li;Zhang, Xiao;Becker, Aaron T.;Kim, Min Jun
• 通讯作者: Kim, Min Jun