Literature DB >> 26210941

Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories.

Caroline Essert1, Sara Fernandez-Vidal2, Antonio Capobianco3, Claire Haegelen4,5, Carine Karachi6, Eric Bardinet2, Maud Marchal7, Pierre Jannin4.   

Abstract

PURPOSE: Automatic methods for preoperative trajectory planning of electrodes in deep brain stimulation are usually based on the search for a path that resolves a set of surgical constraints to propose an optimal trajectory. The relative importance of each surgical constraint is usually defined as weighting parameters that are empirically set beforehand. The objective of this paper is to analyze the use of these parameters thanks to a retrospective study of trajectories manually planned by neurosurgeons. For that purpose, we firstly retrieved weighting factors allowing to match neurosurgeons manually planned choice of trajectory on each retrospective case; secondly, we compared the results from two different hospitals to evaluate their similarity; and thirdly, we compared the trends to the weighting factors empirically set in most current approaches.
METHODS: To retrieve the weighting factors best matching the neurosurgeons manual plannings, we proposed two approaches: one based on a stochastic sampling of the parameters and the other on an exhaustive search. In each case, we obtained a sample of combinations of weighting parameters with a measure of their quality, i.e., the similarity between the automatic trajectory they lead to and the one manually planned by the surgeon as a reference. Visual and statistical analyses were performed on the number of occurrences and on the rank means.
RESULTS: We performed our study on 56 retrospective cases from two different hospitals. We could observe a trend of the occurrence of each weight on the number of occurrences. We also proved that each weight had a significant influence on the ranking. Additionally, we observed no influence of the medical center parameters, suggesting that the trends were comparable in both hospitals. Finally, the obtained trends were confronted to the usual weights chosen by the community, showing some common points but also some discrepancies.
CONCLUSION: The results tend to show a predominance of the choice of a trajectory close to a standard direction. Secondly, the avoidance of the vessels or sulci seems to be sought in the surroundings of the standard position. The avoidance of the ventricles seems to be less predominant, but this could be due to the already reasonable distance between the standard direction and the ventricles. The similarity of results between two medical centers tends to show that it is not an exceptional practice. These results suggest that manual planning software may introduce a bias in the planning by proposing a standard position.

Entities:  

Keywords:  Deep brain stimulation; Neurosurgery; Statistical analysis; Surgical planning; Trajectory optimization

Mesh:

Year:  2015        PMID: 26210941     DOI: 10.1007/s11548-015-1263-5

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  11 in total

1.  CranialVault and its CRAVE tools: a clinical computer assistance system for deep brain stimulation (DBS) therapy.

Authors:  Pierre-François D'Haese; Srivatsan Pallavaram; Rui Li; Michael S Remple; Chris Kao; Joseph S Neimat; Peter E Konrad; Benoit M Dawant
Journal:  Med Image Anal       Date:  2010-08-01       Impact factor: 8.545

2.  A method for planning safe trajectories in image-guided keyhole neurosurgery.

Authors:  Reuben R Shamir; Idit Tamir; Elad Dabool; Leo Joskowicz; Yigal Shoshan
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

3.  Deep brain stimulation for Parkinson's disease: surgical technique and perioperative management.

Authors:  Andre Machado; Ali R Rezai; Brian H Kopell; Robert E Gross; Ashwini D Sharan; Alim-Louis Benabid
Journal:  Mov Disord       Date:  2006-06       Impact factor: 10.338

4.  Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors.

Authors:  Claire Baegert; Caroline Villard; Pascal Schreck; Luc Soler; Afshin Gangi
Journal:  Comput Aided Surg       Date:  2007-03

Review 5.  Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease.

Authors:  Alim Louis Benabid; Stephan Chabardes; John Mitrofanis; Pierre Pollak
Journal:  Lancet Neurol       Date:  2009-01       Impact factor: 44.182

6.  Automatic computation of electrode trajectories for Deep Brain Stimulation: a hybrid symbolic and numerical approach.

Authors:  Caroline Essert; Claire Haegelen; Florent Lalys; Alexandre Abadie; Pierre Jannin
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-08-25       Impact factor: 2.924

7.  A multi-modal approach to computer-assisted deep brain stimulation trajectory planning.

Authors:  Silvain Bériault; Fahd Al Subaie; D Louis Collins; Abbas F Sadikot; G Bruce Pike
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-06-21       Impact factor: 2.924

8.  Relationship between neuropsychological outcome and DBS surgical trajectory and electrode location.

Authors:  Michele K York; Elisabeth A Wilde; Richard Simpson; Joseph Jankovic
Journal:  J Neurol Sci       Date:  2009-09-19       Impact factor: 3.181

9.  Electrical stimulation of the subthalamic nucleus in advanced Parkinson's disease.

Authors:  P Limousin; P Krack; P Pollak; A Benazzouz; C Ardouin; D Hoffmann; A L Benabid
Journal:  N Engl J Med       Date:  1998-10-15       Impact factor: 91.245

10.  Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures.

Authors:  Yuan Liu; Peter E Konrad; Joseph S Neimat; Stephen B Tatter; Hong Yu; Ryan D Datteri; Bennett A Landman; Jack H Noble; Srivatsan Pallavaram; Benoit M Dawant; Pierre-François D'Haese
Journal:  IEEE Trans Biomed Eng       Date:  2014-05-09       Impact factor: 4.538

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  4 in total

1.  Retrospective evaluation and SEEG trajectory analysis for interactive multi-trajectory planner assistant.

Authors:  Davide Scorza; Elena De Momi; Lisa Plaino; Gaetano Amoroso; Gabriele Arnulfo; Massimo Narizzano; Luis Kabongo; Francesco Cardinale
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-07-14       Impact factor: 2.924

2.  Self-guided training for deep brain stimulation planning using objective assessment.

Authors:  Matthew S Holden; Yulong Zhao; Claire Haegelen; Caroline Essert; Sara Fernandez-Vidal; Eric Bardinet; Tamas Ungi; Gabor Fichtinger; Pierre Jannin
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-04-04       Impact factor: 2.924

3.  Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated.

Authors:  Olga Dergachyova; Yulong Zhao; Claire Haegelen; Pierre Jannin; Caroline Essert
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-03-20       Impact factor: 2.924

4.  Automated Steerable Path Planning for Deep Brain Stimulation Safeguarding Fiber Tracts and Deep Gray Matter Nuclei.

Authors:  Alice Segato; Valentina Pieri; Alberto Favaro; Marco Riva; Andrea Falini; Elena De Momi; Antonella Castellano
Journal:  Front Robot AI       Date:  2019-08-06
  4 in total

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