ESM-CT: a precise method for localization of DBS electrodes in CT images

Abstract

Background: Deep brain stimulation (DBS) of the subthalamic nucleus produces variable effects in Parkinson disease. Variation may result from different electrode positions relative to target. Thus, precise electrode localization is crucial when investigating DBS effects.

New method: We developed a semi-automated method, Electrode Shaft Modeling in CT images (ESM-CT) to reconstruct DBS lead trajectories and contact locations. We evaluated methodological sensitivity to operator-dependent steps, robustness to image resampling, and test-retest replicability. ESM-CT was applied in 56 patients to study electrode position change (and relation to time between scans, postoperative subdural air volume, and head tilt during acquisition) between images acquired immediately post-implantation (DBS-CT) and months later (DEL-CT).

Results: Electrode tip localization was robust to image resampling and replicable to within ∼ 0.2 mm on test-retest comparisons. Systematic electrode displacement occurred rostral-ventral-lateral between DBS-CT and DEL-CT scans. Head angle was a major explanatory factor (p < 0.001,Pearson's r = 0.46, both sides) and volume of subdural air weakly predicted electrode displacement (p = 0.02,r = 0.29:p = 0.1,r = 0.25 for left:right). Modeled shaft curvature was slightly greater in DEL-CT. Magnitude of displacement and degree of curvature were independent of elapsed time between scans.

Comparison with existing methods: Comparison of ESM-CT against two existing methods revealed systematic differences in one coordinate (1 ± 0.3 mm,p < 0.001) for one method and in three coordinates for another method (x:0.1 ± 0.1 mm, y:0.4 ± 0.2 mm, z:0.4 ± 0.2 mm, p < 10^-10). Within-method coordinate variability across participants is similar.

Conclusion: We describe a robust and precise method for CT DBS contact localization. Application revealed that acquisition head angle significantly impacts electrode position. DBS localization schemes should account for head angle.

Keywords: Brain shift; CT; Contact localization; Deep brain stimulation (DBS); Subthalamic nucleus (STN).

Figure 1.

Top row: (A) sagittal slice from a typical DBS-CT; (B) sagittal slice from a typical DEL-CT; bottom row: schematic relative head position during scan for slice above. Vertical white lines depict scanning plane. Note the difference in acquisition angle α between DBS-CT and DEL-CT.

Figure 2.

Configuration of model 3389 electrode array. Red dot indicates the location used for electrode migration analysis.

Figure 3.

Implant trajectory at Step 1 of ESM-CT procedure. Segment S=[P_d; P_p] initializes the trajectory. The curve represents trajectory interpolation found at the end of Step 1.

Figure 4.

Verification of ESM-CT outcome. Moving average of intensity in a CT image sampled along different DBS implant trajectory estimates: initial straight line estimate (orange), straight line found by ESM-CT (blue), final curve found by ESM-CT (green). X axis represents fractional position along the implant, with x = 0 at proximal end and x = 1 at the distal end. Dotted vertical lines indicate the detected boundaries of the contact array.

Figure 5.

Spatially coregistered MRI (A), DBS-CT (B) and DEL-CT (C) in MNI152 space. Left to right: sagittal, coronal and axial views. Red edge contours were computed from MRI and superimposed over DBS-CT and DEL-CT.

Figure 6.

Clusters of intracranial air on a DBS-CT image identified by an operator. Left to right: sagittal, coronal and axial views. In this case one cluster (brown) was identified on the right and one (blue) on the left side.

Figure 7.

Obtaining contact locations in MNI152 space. Arrows indicate information flow.

Figure 8.

Mean and standard deviation of discrepancy in distal contact location computed by ESM-CT in original and rotated images. The discrepancy was averaged over nine tested images, each rotated in 1° increments between −30° and +30° about the x (sagittal direction in image space), y (coronal direction in image space), and z axis (axial direction in image space).

Figure 9.

3D rendering of a skull based on the average template of 56 CT-DBS images, co-aligned in common space. Red: average of DBS implants in CT-DBS images. Implants were co-aligned separately for each side using rigid body transform, and averaged across 56 study participants. Green: Average of DBS implants in corresponding 56 CT-DEL images, resampled to the CT-DBS average template space using computed 6-DOF rigid body CT-DBS↔CT-DEL transform. Note the diffence in average CT-DBS and CT-DEL implant trajectories.

10.

Linear models for (A, B) implant curvature change (ΔK), and (C, D) for z displacement (Δz) in MNI152 space. All models were computed for 56 PD study participants, with volume of subdural air A in DBS-CT and change in acquisition angle Δα as covariates. Actual value (x axis) vs linear model prediction (y axis) is plotted.