Evaluation of the PaCER Algorithm for Postoperative Subthalamic Nucleus Deep Brain Stimulation Electrode...

Area of Science:

• Neurosurgery
• Medical Imaging
• Computational Neuroscience

Background:

• Deep Brain Stimulation (DBS) is a crucial therapy for various movement disorders.
• Accurate localization of implanted electrodes relative to target brain structures is vital for optimizing patient outcomes.
• Current methods for electrode localization can be time-consuming and require expert manual input.

Purpose of the Study:

• To evaluate the performance of an open-source automatic algorithm, PaCER, for localizing Deep Brain Stimulation electrodes.
• To assess the accuracy of the PaCER algorithm by comparing its results to manual expert markups.
• To determine the suitability of automated electrode localization for clinical application in DBS therapy.

Main Methods:

• Utilized an open-source automatic algorithm (PaCER) for electrode localization on Computed Tomography (CT) imaging.
• Co-registered CT images with pre-operative Magnetic Resonance Imaging (MRI) for anatomical reference.
• Validated the algorithm's accuracy against manual delineations performed by experts on a dataset of 111 participants.

Main Results:

• The modified PaCER algorithm demonstrated high accuracy in localizing DBS electrodes.
• Median localization error was below 0.191 mm, with an interquartile range of 0.698 mm.
• The observed error was less than the voxel resolution of the imaging data (1 mm³).

Conclusions:

• The automated PaCER algorithm is suitable for precise Deep Brain Stimulation electrode localization.
• Accurate automated localization can guide neurologists in optimizing electrical stimulation parameters for improved patient outcomes.
• This technology can also inform decisions regarding electrode placement or re-implantation if initial placement is suboptimal.