Abstract
BACKGROUND
Automatic segmentation is gaining relevancy in image-based targeting of neural structures.
OBJECTIVE
To evaluate its feasibility, we retrospectively analyzed the concordance of magnetic resonance imaging (MRI)-based automatic segmentation of the subthalamic nucleus (STN) and intraoperative microelectrode recordings (MERs).
METHODS
Electrodes (n = 60) for deep brain stimulation were implanted in the STN of patients (n = 30; median age 57 yr) with Parkinson disease (n = 29) or rapid-onset dystonia parkinsonism (n = 1). Elements (Brainlab, Munich, Germany) were used to segment the STN, using 2 volumetric T1 (±contrast) and volumetric T2 images as input. The stereotactic computed tomography was coregistered with the imaging, and the original stereotactic coordinates were imported. MERs (0.5-1 mm steps) along the anterior, central, and lateral trajectories were used to determine differences between the image-segmented STN boundary and MER-based STN entry and exit.
RESULTS
Of 175 trajectories, 105 penetrated or touched (≤0.7 mm) the STN. The overall median deviation between the segmented STN boundary and electrophysiological recordings was 1.1 mm for MER-based STN entry and 2.0 mm for STN exit. Regarding the entry point of the STN, there was no statistically significant difference between MRI-based automatic segmentation and the electrophysiological trajectories analyzed with intraoperative MER. The exit point was significantly different between both methods in the central and lateral trajectories.
CONCLUSION
MRI-based automatic segmentation of the STN is a viable, patient-specific targeting approach that can be used alongside traditional targeting methods in deep brain stimulation to support preoperative planning and visualization of target structures and aid postoperative optimization of programming.
CranialVault and its CRAVE tools: A clinical computer assistance system for deep brain stimulation (DBS) therapy
