Autonomous Trajectory Planning for External Ventricular Drain Placement.

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Anatomy

Background:

• External ventricular drain (EVD) placement is a common neurosurgical procedure for hydrocephalus, traumatic brain injury, and stroke.
• Improving the safety and accuracy of EVD placement is crucial, especially in urgent situations.
• Current methods rely on experienced surgeons, with potential for variability.

Purpose of the Study:

• To develop and evaluate a novel computer algorithm for autonomous planning of EVD placement.
• The algorithm identifies critical anatomy and plans optimal trajectories on clinical CT scans.

Main Methods:

• The algorithm was tested on noncontrast CT scans from 300 normal patients and 43 patients with acute intracranial hemorrhage.
• Automated planning utilized a custom 2-tiered heuristic with template selection and nonlinear image registration.
• Accuracy was assessed by comparing planned trajectories to anatomical landmarks.

Main Results:

• Automated EVD planning demonstrated high accuracy: 99% in normal scans and 95% in hemorrhage cases.
• The algorithm successfully identified target anatomy and planned trajectories.
• Quantitative analysis of planned trajectories showed consistency across patient demographics.

Conclusions:

• A novel computer algorithm can accurately plan external ventricular drain (EVD) trajectories using CT imaging.
• This automated approach shows significant potential to enhance the safety and efficiency of EVD placement.
• The algorithm's high accuracy in both normal and pathological scans supports its clinical applicability.