Vrooijink Gustaaf J, Denasi Alper, Grandjean Jan G, Misra Sarthak
Department of Biomechanical Engineering, University of Twente, The Netherlands.
Department of Cardiothoracic Surgery, Thorax Centre Twente, The Netherlands.
Int J Rob Res. 2017 Feb;36(2):193-209. doi: 10.1177/0278364917691113. Epub 2017 Feb 1.
Minimally invasive surgery (MIS) during cardiovascular interventions reduces trauma and enables the treatment of high-risk patients who were initially denied surgery. However, restricted access, reduced visibility and control of the instrument at the treatment locations limits the performance and capabilities of such interventions during MIS. Therefore, the demand for technology such as steerable sheaths or catheters that assist the clinician during the procedure is increasing. In this study, we present and evaluate a robotically actuated delivery sheath (RADS) capable of autonomously and accurately compensating for beating heart motions by using a model-predictive control (MPC) strategy. We develop kinematic models and present online ultrasound segmentation of the RADS that are integrated with the MPC strategy. As a case study, we use pre-operative ultrasound images from a patient to extract motion profiles of the aortic heart valve (AHV). This allows the MPC strategy to anticipate for AHV motions. Further, mechanical hysteresis in the steering mechanism is compensated for in order to improve tip positioning accuracy. The novel integrated system is capable of controlling the articulating tip of the RADS to assist the clinician during cardiovascular surgery. Experiments demonstrate that the RADS follows the AHV motion with a mean positioning error of 1.68 mm. The presented modelling, imaging and control framework could be adapted and applied to a range of continuum-style robots and catheters for various cardiovascular interventions.
心血管介入手术中的微创手术(MIS)减少了创伤,并能够治疗那些最初被拒绝手术的高危患者。然而,在治疗部位有限的操作空间、降低的可视性以及器械控制能力限制了MIS期间此类介入手术的性能和能力。因此,对诸如在手术过程中协助临床医生的可转向鞘管或导管等技术的需求正在增加。在本研究中,我们展示并评估了一种能够通过使用模型预测控制(MPC)策略自主且准确地补偿心脏跳动运动的机器人驱动输送鞘管(RADS)。我们开发了运动学模型,并展示了与MPC策略集成的RADS在线超声分割。作为一个案例研究,我们使用来自一名患者的术前超声图像来提取主动脉心脏瓣膜(AHV)的运动轮廓。这使得MPC策略能够预测AHV的运动。此外,对转向机构中的机械滞后进行了补偿,以提高尖端定位精度。这种新型集成系统能够控制RADS的关节式尖端,以在心血管手术中协助临床医生。实验表明,RADS跟随AHV运动时的平均定位误差为1.68毫米。所提出的建模、成像和控制框架可以进行调整,并应用于一系列用于各种心血管介入手术的连续体式机器人和导管。