Robotic Assisted Imaging

机器人辅助成像

• 批准号: EP/P012841/1
• 负责人: Danail Stoyanov
• 金额: $ 157.91万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP012841%2F1
• 关键词: Robotic; Assisted; Imaging

The paradigm of modern surgical treatment is to reduce the invasive trauma of procedures by using small keyhole ports to enter the body. Robotic assistant systems provide tele-manipulated instruments that facilitate minimally invasive surgery by improving the ergonomics, dexterity and precision of controlling manual keyhole surgery instruments. Robotic surgery is now common for minimally invasive prostate and renal cancer procedures. But imaging inside the body is currently restricted by the access port and only provides information at visible organ surfaces which is often insufficient for easy localisation within the anatomy and avoiding inadvertent damage to healthy tissues. This project will develop robotic assisted imaging which will exploit the autonomy and actuation capabilities provided by robotic platforms, to optimise the images that can be acquired by current surgical imaging modalities. In the context of robotic assisted surgery, now an established surgical discipline, advanced imaging can help the surgeon to operate more safely and efficiently by allowing the identification of structures that need to be preserved while guiding the surgeon to anatomical targets that need to be removed. Providing better imaging and integration with the robotic system will result in multiple patient benefits by ensuring safe, accurate surgical actions that lead to improved outcomes.To expose this functionality, new theory, computing, control algorithms and real-time implementations are needed to underpin the integration of imaging and robotic systems within dynamic environments. Information observed by the imaging sensor needs to feed back into the robotic control loop to guide automatic sensor positioning and movement that maintains the alignment of the sensor to moving organs and structures. This level of automation is largely unexplored in robotic assisted surgery at present because it involves multiple challenges in visual inference, reconstruction and tracking; calibration and re-calibration of sensors and various robot kinematic strategies; integration with surgical workflow and user studies. Combined with the use of pre-procedural planning, robotic assisted imaging can lead to pre-planned imaging choices that are motivated by different clinical needs. As well as having direct applications in surgery, the robotic assisted imaging paradigm will be applicable to many other sectors transformed by robotics, for example manufacturing or inspection, especially when working within non-rigid environments. For this cross sector impact to be achieved the project will build the deep theoretical and robust software platforms that are ideally suited for foundational fellowship support.

现代外科治疗的范例是通过使用小锁孔端口进入身体来减少手术的侵入性创伤。机器人辅助系统提供远程操纵的器械，其通过改善控制手动锁孔手术器械的人体工程学、灵活性和精确度来促进微创手术。机器人手术现在在微创前列腺癌和肾癌手术中很常见。但是，体内成像目前受到进入端口的限制，并且仅提供可见器官表面处的信息，这通常不足以在解剖结构内容易地定位并避免对健康组织的意外损伤。该项目将开发机器人辅助成像，利用机器人平台提供的自主性和驱动能力，优化当前手术成像模式可以获取的图像。在机器人辅助手术的背景下，现在已经建立了手术学科，先进的成像可以帮助外科医生更安全和有效地操作，允许识别需要保留的结构，同时引导外科医生到需要切除的解剖目标。提供更好的成像和与机器人系统的集成将通过确保安全、准确的手术操作带来更好的结果，从而为患者带来多重利益。为了实现这一功能，需要新的理论、计算、控制算法和实时实现来支持成像和机器人系统在动态环境中的集成。成像传感器观察到的信息需要反馈到机器人控制回路中，以引导自动传感器定位和移动，从而保持传感器与移动器官和结构的对准。这种自动化水平目前在机器人辅助手术中基本上未被探索，因为它涉及视觉推理，重建和跟踪的多重挑战;传感器和各种机器人运动学策略的校准和重新校准;与手术工作流程和用户研究的集成。结合使用术前计划，机器人辅助成像可以导致由不同临床需求驱动的预先计划的成像选择。除了在外科手术中的直接应用外，机器人辅助成像范式还将适用于机器人技术改造的许多其他领域，例如制造或检查，特别是在非刚性环境中工作时。为了实现这种跨部门的影响，该项目将建立深厚的理论和强大的软件平台，非常适合基础奖学金支持。

项目成果

Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues

• DOI: 10.1016/j.tgie.2019.150636
• 发表时间: 2020-04-01
• 期刊: TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY
• 影响因子: 2.4
• 作者: Ahmad, Omer F.;Stoyanov, Danail;Lovat, Laurence B.
• 通讯作者: Lovat, Laurence B.

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.

• DOI: 10.1055/a-1306-7590
• 发表时间: 2021-09
• 期刊: Endoscopy
• 影响因子: 9.3
• 作者: Ahmad OF;Mori Y;Misawa M;Kudo SE;Anderson JT;Bernal J;Berzin TM;Bisschops R;Byrne MF;Chen PJ;East JE;Eelbode T;Elson DS;Gurudu SR;Histace A;Karnes WE;Repici A;Singh R;Valdastri P;Wallace MB;Wang P;Stoyanov D;Lovat LB
• 通讯作者: Lovat LB

Weakly Supervised Recognition of Surgical Gestures

• DOI: 10.1109/icra.2019.8793696
• 发表时间: 2019-05
• 期刊: 2019 International Conference on Robotics and Automation (ICRA)
• 作者: Beatrice van Amsterdam;H. Nakawala;E. Momi;D. Stoyanov

Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part IV

医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第四部分

• DOI: 10.1007/978-3-031-16440-8_27
• 发表时间: 2022
• 作者: Avisdris N

Keep Your Eye on the Best: Contrastive Regression Transformer for Skill Assessment in Robotic Surgery

• DOI: 10.1109/lra.2023.3242466
• 发表时间: 2023-03-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Anastasiou, Dimitrios;Jin, Yueming;Mazomenos, Evangelos
• 通讯作者: Mazomenos, Evangelos