Project Week 25/Steerable Catheters Path Planner Extension for Brain Surgery Applications

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Key Investigators

Project Description

Objective Approach and Plan Progress and Next Steps

The project consists in a reliable and time-efficient pre-operative path planner for steerable catheters, intended to be used in neurosurgical applications. The algorithm estimates a pathway from an entry to a target point within the 3D space defined by the brain volume in accordance to specific optimality parameters and constraints, as the geometric and kynodynamic characteristics of the catheter, the distance from inner brain structures (e.g. the main blood vessels) and the total path length.

The workflow consists in the following steps:

  • Definition of a risk-based distance map based on a multimodal brain imaging
  • Piecewise-linear path estimation, exploiting the 3D implementation of [1]
  • Path interpolation via NURBS curves (a 2D application of the method can be found in [2]) accounting for catheter cinematic and safety constraints
  • Uncertainties map computation, to account for path tracking error during the catheter insertion

Goals achieved:

  • Deformable image registration via Plastimatch
  • Blood vessel segmentation through VMTK
  • 3D BIT* path planner implementation
  • Path interpolation
  • Surgeon eye view

Future steps:

  1. DTI data integration
  2. Robust path interpolation considering catheter cinematic constraints
  3. Uncertainties map

FavaroPinzi interface.png FavaroPinzi path.mov


Illustrations

Eden2020 steerable catheter:
2.5mm Blue Catheter

Video

Surgeon eye view: FavaroPinzi view.gif

Background and References

The proposed path planning solution finds a possible direct implementation in the EDEN2020 project, a recently founded EU project (GA No. 688279), which aims to develop an innovative system from treating glioblastoma – a highly malignant tumor involving the central nervous system – through a pioneering steerable catheter (see figure above).

  1. Gammell, Jonathan D., Siddhartha S. Srinivasa, and Timothy D. Barfoot. "Batch informed trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs." Robotics and Automation (ICRA), 2015 IEEE International Conference on. IEEE, 2015
  2. Jalel, Sawssen, Philippe Marthon, and Atef Hamouda. "A new path generation algorithm based on accurate NURBS curves." International Journal of Advanced Robotic Systems 13.2 (2016): 75.