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An Anatomy-specific Guidewire Shaping Robot for Improved Vascular Navigation

Aabha Tamhankar, Jay Patil, Giovanni Pittiglio

TL;DR

The paper addresses the variability and skill-dependence of neuroendovascular guidewire shaping by introducing an autonomous wire-shaping robot and a data-driven mapping from desired wire geometries to robot actions. The three-module design performs roll–bend–advance shaping, and calibration identifies a per-wire bend parameter to predict tip geometry. Experiments show sub-millimeter accuracy in 2D (RMS $0.56$ mm) for planar shapes and validate 3D tip shaping with in-vitro navigation through complex cerebral vasculature. This work lays the groundwork for anatomy-specific, repeatable wire shaping and enables future image-based planning and closed-loop control in clinical workflows.

Abstract

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).

An Anatomy-specific Guidewire Shaping Robot for Improved Vascular Navigation

TL;DR

The paper addresses the variability and skill-dependence of neuroendovascular guidewire shaping by introducing an autonomous wire-shaping robot and a data-driven mapping from desired wire geometries to robot actions. The three-module design performs roll–bend–advance shaping, and calibration identifies a per-wire bend parameter to predict tip geometry. Experiments show sub-millimeter accuracy in 2D (RMS mm) for planar shapes and validate 3D tip shaping with in-vitro navigation through complex cerebral vasculature. This work lays the groundwork for anatomy-specific, repeatable wire shaping and enables future image-based planning and closed-loop control in clinical workflows.

Abstract

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).
Paper Structure (9 sections, 6 equations, 7 figures, 1 table)

This paper contains 9 sections, 6 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Guidewire Shaping Robot Design
  3. Wire Shaping Model
  4. Model Tuning
  5. Experimental Validation
  6. Calibration
  7. Model Validation
  8. Demonstration of Navigation In-vitro
  9. Conclusions

Figures (7)

  • Figure 1: Catheterization for neuroendovascular treatments with alternative access routes via arm (radial) or leg (femoral). Example of guidewire navigation past the into the .
  • Figure 2: The proposed guidewire shaping robot.
  • Figure 3: Schematic representation of the shapable wire.
  • Figure 4: Measured end-to-end chord length ($\bar{C}$) of the 10 segment arc of three identical wires.
  • Figure 5: Predicted (model) versus realized centerlines for the a) C, b) S, c) Angled, and d) Hook shapes using the calibrated bend $\hat{\theta}$.
  • ...and 2 more figures