Automatic Reconstruction of Deep Brain Stimulation Lead Trajectories From CT Images Using Tracking and Morphological Analysis

Abstract

Deep brain stimulation (DBS) is an effective treatment for neurological disorders, and accurately reconstructing the DBS lead trajectories is crucial for MRI compatibility assessment and surgical planning. This paper presents a novel fully automated framework for reconstructing DBS lead trajectories from postoperative CT images. The leads were first segmented by thresholding, but would be fused together somewhere. Mean curvature analysis of multi-layer CT number isosurfaces was introduced to effectively address lead fusion, due to the different topological characteristics of the isosurfaces in and out of the fusion regions. The position of electrode contacts was determined through morphological analysis to get the starting point and the initial direction for trajectory tracking. The next trajectory point was derived by calculating the weighted average coordinates of the candidate points, using the distance from the current estimated trajectory and the CT number as weights. This method has demonstrated high accuracy and efficiency, successfully and automatically reconstructing complex bilateral trajectories for 13 patient cases in less than 10 minutes with errors less than 1 mm. This work overcomes the limitations of existing semi-automatic techniques that require extensive manual intervention. It paves the way for optimizing DBS lead trajectory to reduce tissue heating and image artifacts, which will contribute to neuroimaging studies and improve clinical outcomes. Code for our proposed algorithm is publicly available on Github.

Keywords

• CT number
• deep brain stimulation
• mean curvature
• trajectory tracking