Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province, 310027, People's Republic of China.
Department of Urology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China.
Int J Comput Assist Radiol Surg. 2022 Oct;17(10):1787-1799. doi: 10.1007/s11548-022-02670-8. Epub 2022 Jul 17.
Flexible ureteroscopy (FURS) plays an important role in the diagnosis and treatment of urological diseases. However, manipulating a flexible ureteroscope to the target quickly and safely may be challenging because of the tortuous lumen or poor visibility. Thus, information on the shape of the anterior part of a flexible ureteroscope in addition to the real-time pose is needed to perform accurate maneuvering in the lumen with minimal impingement on the inner renal wall and resulting tissue damage in FURS.
An adaptive mixed-order Bézier curve fitting algorithm and electromagnetic tracking (EMT) technique were developed for shape estimation utilizing the length of the anterior part, kinematic constraints and the pose information provided by two electromagnetic (EM) sensors mounted at the tip and base of the anterior part. A series of experiments were performed to qualitatively and quantitatively verify the validity of our method. Moreover, algorithm threshold conditions with reference significance under various shape cases were studied.
The performance of our method was evaluated based on 19 representative planar bending shapes that often appear in FURS and eight non-planar shapes, yielding an average error (AE) of 1.0 mm. Moreover, the experiments proved the feasibility of applying our method in cases in which large bending angles (near 270 degrees) occur.
Based on data from two EM sensors mounted at the tip and base of the anterior part of a flexible ureteroscope, the proposed algorithm adaptively selects a cubic or quartic Bézier curve to fit the shape of the anterior part. Experimental results prove the feasibility of our shape estimation method over a broad bending range. The proposed method demonstrates significant potential for use in ureteroscopic navigation systems and robot-assisted surgery.
软性输尿管镜（FURS）在诊断和治疗泌尿科疾病中起着重要作用。然而，由于输尿管管腔的迂曲或可视性差，快速、安全地将软性输尿管镜操作到目标位置可能具有挑战性。因此，在 FURS 中进行精确操作时，除了实时位姿外，还需要了解软性输尿管镜前段的形状信息，以最小的内肾壁撞击和由此产生的组织损伤来实现准确的操作。
开发了一种自适应混合阶贝塞尔曲线拟合算法和电磁跟踪（EMT）技术，利用前段的长度、运动学约束以及安装在前段尖端和基部的两个电磁（EM）传感器提供的位姿信息来进行形状估计。进行了一系列实验，以定性和定量验证我们方法的有效性。此外，研究了在各种形状情况下具有参考意义的算法阈值条件。
基于在 FURS 中经常出现的 19 种有代表性的平面弯曲形状和 8 种非平面形状进行评估，我们的方法的平均误差（AE）为 1.0 毫米。此外，实验证明了我们的方法在出现大弯曲角度（接近 270 度）的情况下的可行性。
基于安装在软性输尿管镜前段尖端和基部的两个 EM 传感器的数据，所提出的算法自适应地选择三次或四次贝塞尔曲线来拟合前段的形状。实验结果证明了我们的形状估计方法在广泛的弯曲范围内的可行性。该方法在输尿管镜导航系统和机器人辅助手术中具有重要的应用潜力。
