一种针对解剖结构的导管引导机器人以提高血管导航
An Anatomy-specific Guidewire Shaping Robot for Improved Vascular Navigation
AI总结：
本文提出了一种针对解剖结构的导管引导机器人，通过模型预测和实验验证，实现了高精度的导管形状塑形和复杂血管导航能力。
摘要：
神经内血管穿刺常常依赖于被动微导管，这些导管在操作台后端手工塑形，然后用于跟踪微导管到达目标。神经内血管外科医生通过查看患者术前图像并利用经验来确定导管的形状，以帮助到达目标。这一过程在复杂的解剖结构中特别复杂，并且严重依赖于外科医生的水平。为了实现标准化的自主塑形，我们提出了一种操作台导管塑形机器人，能够产生导航特定的导管配置。我们提出了一种模型，可以将所需的导管形状映射到机器人动作，并通过实验数据进行校准。我们展示了机器人能够产生临床常用的导管末端几何形状（C、S、角度、钩形）并将其与模型预测的形状在2D中验证。我们的模型在所有形状上与实验结果的均方误差为0.56毫米。我们还展示了三维导管末端塑形能力和从岩骨内颈动脉（ICA）到后交通动脉（PComm）的复杂内腔导航能力。
英文摘要：
Neuroendovascular access often relies on passive microwires that are hand-shaped at the back table and then used to track a microcatheter to the target. Neuroendovascular surgeons determine the shape of the wire by examining the patient pre-operative images and using their experience to identify anatomy specific shapes of the wire that would facilitate reaching the target. This procedure is particularly complex in convoluted anatomical structures and is heavily dependent on the level of expertise of the surgeon. Towards enabling standardized autonomous shaping, we present a bench-top guidewire shaping robot capable of producing navigation-specific desired wire configurations. We present a model that can map the desired wire shape into robot actions, calibrated using experimental data. We show that the robot can produce clinically common tip geometries (C, S, Angled, Hook) and validate them with respect to the model-predicted shapes in 2D. Our model predicts the shape with a Root Mean Square (RMS) error of 0.56mm across all shapes when compared to the experimental results. We also demonstrate 3D tip shaping capabilities and the ability to traverse complex endoluminal navigation from the petrous Internal Carotid Artery (ICA) to the Posterior Communicating Artery (PComm).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
