Introduction Successful device selection for neuro endovascular surgery is based on patient anatomy. Pre-operative imaging and knowledge of catheter and wire properties (shape, size, and relative stiffness) influence device choice. Even with experience, a variety of circumstances may cause the operator to exchange devices, including challenging anatomy or inadequate support. Objective evaluation of patient anatomy, such as three-dimensional measurements, and quantitative evaluation of devices would allow for accurate modeling of device performance in a given patient’s anatomy. While advancements in device design broaden the potential use cases of endovascular surgery, they also make subjectively selecting the correct device in an ever-growing catalog more difficult. We set out to develop a computational model that incorporates individual catheter and wire properties to accurately simulate device navigation in comparison to a 3D printed model.
Materials and Methods On a set of microwires, guidewires, and catheters, we marked a one-dimensional coordinate system along device length for all measurements to refer to. Tip curvature and shape was described via image analysis. We measured the position dependent flexural rigidity of each devices by performing three-point-bending tests. These measurements were used to develop a novel computational model that incorporated these device properties which we validated against a 3D printed physical counterpart. We computed the deviation of our simulated path relative to device insertion in the printed boundary.
Results We identified a range of device properties that affect navigation and device performance in challenging anatomies. Devices with variable distal shapes exhibited tip lengths between 9.6 mm and 86.9 mm. These devices had radii of curvature between 4.1 mm and 26.4 mm. Flexural rigidities varied between 16.85 N mm2 and 1075 N mm2. Figure 1 shows how a simulated device compared to its physical counterpart.
Conclusion This study provides a quantitative review on endovascular devices’ unique properties. These properties can be used to simulate how devices would navigate through a 3D printed model. Establishing an accurate model is the necessary first step to predict device performance in novel boundaries with varying mechanics.
Disclosures J. Rifkin: None. N. McCann: None. J. Vargas: 2; C; Viz.Ai, Imperative Care. 4; C; Viz.Ai. R. Kellogg: 1; C; University of Virginia. 2; C; Imperative Care, Cerenovus, Viz.Ai. 4; C; Viz.ai. M. Panzer: None.
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