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. 2015 Jun;76(6):756-65.
doi: 10.1227/NEU.0000000000000714.

Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease

Srivatsan Pallavaram  1 , Pierre-François DʼHaese, Wendell Lake, Peter E Konrad, Benoit M Dawant, Joseph S Neimat
Affiliations

Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease

Srivatsan Pallavaram et al. Neurosurgery. 2015 Jun.

Abstract

Background: Finding the optimal location for the implantation of the electrode in deep brain stimulation (DBS) surgery is crucial for maximizing the therapeutic benefit to the patient. Such targeting is challenging for several reasons, including anatomic variability between patients as well as the lack of consensus about the location of the optimal target.

Objective: To compare the performance of popular manual targeting methods against a fully automatic nonrigid image registration-based approach.

Methods: In 71 Parkinson disease subthalamic nucleus (STN)-DBS implantations, an experienced functional neurosurgeon selected the target manually using 3 different approaches: indirect targeting using standard stereotactic coordinates, direct targeting based on the patient magnetic resonance imaging, and indirect targeting relative to the red nucleus. Targets were also automatically predicted by using a leave-one-out approach to populate the CranialVault atlas with the use of nonrigid image registration. The different targeting methods were compared against the location of the final active contact, determined through iterative clinical programming in each individual patient.

Results: Targeting by using standard stereotactic coordinates corresponding to the center of the motor territory of the STN had the largest targeting error (3.69 mm), followed by direct targeting (3.44 mm), average stereotactic coordinates of active contacts from this study (3.02 mm), red nucleus-based targeting (2.75 mm), and nonrigid image registration-based automatic predictions using the CranialVault atlas (2.70 mm). The CranialVault atlas method had statistically smaller variance than all manual approaches.

Conclusion: Fully automatic targeting based on nonrigid image registration with the use of the CranialVault atlas is as accurate and more precise than popular manual methods for STN-DBS.

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Conflict of interest statement

Conflict of Interest: P-F. D'Haese, P.E. Konrad, and B.M. Dawant are founding members and stock holders, and S. Pallavaram is a stockholder in Neurotargeting, LLC that licenses the software suite from Vanderbilt University that was used in this study for data collection. P.E. Konrad is also a Consultant for Medtronic Neuromodulation.

Figures

Figure 1
Figure 1
WayPoint Navigator software made available to the surgeons for clinical planning as well as for manual target slections in this study. A 3D rendering of the platform and the patient's head based on the CT is also shown along with a sample trajectory for an STN-DBS patient.
Figure 2
Figure 2
Individual contacts in a 3389 lead extracted from the delayed post-op CT and overlaid on the pre-op MRI using rigid registration.
Figure 3
Figure 3
Flowchart showing how target predictions based on non-rigid image registration are made using the CranialVault atlas by projecting contacts positions from a population of patients onto a new patient.
Figure 4
Figure 4
Bar chart showing the average and standard deviation of the Euclidean distances for different targeting methods from active contacts in 71 STN-DBS implants.
Figure 5
Figure 5
Coronal and axial views showing the location of the atlas centroid overlaid on the 3D renderings of the segmentations of STN and SNr.
See this image and copyright information in PMC

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