title: 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.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: *Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee; ‡Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Srivatsan
authors.initials: S
authors.lastname: Pallavaram
authors.forename: Pierre-François
authors.initials: PF
authors.lastname: DʼHaese
authors.forename: Wendell
authors.initials: W
authors.lastname: Lake
authors.forename: Peter E
authors.initials: PE
authors.lastname: Konrad
authors.forename: Benoit M
authors.initials: BM
authors.lastname: Dawant
authors.forename: Joseph S
authors.initials: JS
authors.lastname: Neimat
mesh_terms: D000368:Aged; D046690:Deep Brain Stimulation / Q000379:methods*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008875:Middle Aged; D010300:Parkinson Disease / Q000601:surgery; D013238:Stereotaxic Techniques; D020531:Subthalamic Nucleus / Q000502:physiology; D025321:Surgery, Computer-Assisted / Q000379:methods*