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).

翻译：神经血管介入手术通常依赖被动式微导丝，这些导丝在操作台进行手工塑形后，用于引导微导管追踪至目标位置。神经血管介入外科医生通过分析患者术前影像，并结合自身经验，确定有助于抵达目标血管的、针对特定解剖结构的导丝形状。该操作在迂曲的解剖结构中尤为复杂，且高度依赖术者的经验水平。为实现标准化自主塑形，我们提出了一种工作台导丝塑形机器人，能够生成适用于导航的预期导丝构型。我们提出了一个模型，可将预期导丝形状映射为机器人动作，并利用实验数据进行校准。我们展示了该机器人能够生成临床常见的导丝头端几何形状（C形、S形、角度形、钩形），并在二维平面上通过模型预测形状进行了验证。与实验结果相比，我们的模型对所有形状的预测均方根误差为0.56毫米。我们还展示了三维头端塑形能力，以及完成从岩段颈内动脉到后交通动脉的复杂腔内导航的能力。