Moreira Pedro, Misra Sarthak
Department of Biomechanical Engineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Horstring W-208, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands,
Ann Biomed Eng. 2015 Aug;43(8):1716-26. doi: 10.1007/s10439-014-1203-5. Epub 2014 Dec 3.
Needle-based procedures are commonly performed during minimally invasive surgery for treatment and diagnosis. Accurate needle tip placement is important for the success of the procedures. Misplacement of the needle tip might cause unsuccessful treatment or misdiagnosis. Robot-assisted needle insertion systems have been developed in order to steer flexible bevel-tipped needles. However, current systems depend on the information of maximum needle curvature, which is estimated by performing prior insertions. This work presents a new three-dimensional flexible needle steering system which integrates an optimal steering control, ultrasound-based needle tracking system, needle deflection model, online needle curvature estimation and offline curvature estimation based on biomechanics properties. The online and the offline curvature estimations are used to update the steering control in real time. The system is evaluated by experiments in gelatin phantoms and biological tissues (chicken breast tissues). The average targeting error in gelatin phantoms is 0.42 ± 0.17 mm, and in biological tissues is 1.63 ± 0.29 mm. The system is able to accurately steer a flexible needle in multi-layer phantoms and biological tissues without performing prior insertions to estimate the maximum needle curvature.
基于针的操作在微创手术的治疗和诊断过程中经常进行。准确的针尖放置对于这些操作的成功至关重要。针尖放置不当可能会导致治疗失败或误诊。为了操控柔性斜角针尖针,已经开发了机器人辅助针插入系统。然而,当前系统依赖于通过预先插入来估计的最大针曲率信息。这项工作提出了一种新的三维柔性针操控系统,该系统集成了最优操控控制、基于超声的针跟踪系统、针偏转模型、在线针曲率估计以及基于生物力学特性的离线曲率估计。在线和离线曲率估计用于实时更新操控控制。该系统通过在明胶模型和生物组织(鸡胸组织)中的实验进行评估。在明胶模型中的平均靶向误差为0.42±0.17毫米,在生物组织中为1.63±0.29毫米。该系统能够在多层模型和生物组织中准确操控柔性针,而无需预先插入来估计最大针曲率。