Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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More Accurate Neuronavigation Data Provided by Biomechanical Modeling Instead of Rigid Registration

Institution:
1Intelligent Systems for Medicine Laboratory, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.
2Computational Radiology Laboratory, Children's Hospital, Harvard Medical School, Boston, MA, USA.
3Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School Boston, MA, USA.
4Department of Neurosurgery, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia.
5School of Anatomy, Physiology and Human Biology, The University of Western Australia, Perth, Western Australia, Australia;.
6Centre for Cardiovascular Science, The University of Edinburgh, Scotland.
7Institute of Mechanics and Advanced Materials, School of Engineering, Cardiff University, Cardiff, Wales, UK.
Publication Date:
Jun-2014
Journal:
J Neurosurg
Volume Number:
120
Issue Number:
6
Pages:
1477-83
Citation:
J Neurosurg. 2014 Jun;120(6):1477-83.
PubMed ID:
24460486
PMCID:
PMC4386882
Keywords:
Brain shift, image-guided neurosurgery, nonlinear biomechanical models, nonrigid registration, finite element method
Appears in Collections:
NAC, NA-MIC, NCIGT, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 EB008015/EB/NIBIB NIH HHS/United States
R01 LM010033/LM/NLM NIH HHS/United States
U41 RR019703/RR/NCRR NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Garlapati R.R., Roy A., Joldes G.R., Wittek A., Mostayed A., Doyle B., Warfield S.K., Kikinis R., Knuckey N., Bunt S., Miller K. More Accurate Neuronavigation Data Provided by Biomechanical Modeling Instead of Rigid Registration. J Neurosurg. 2014 Jun;120(6):1477-83. PMID: 24460486. PMCID: PMC4386882.
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It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this paper, the accuracy of registration results obtained using comprehensive biomechanical models is compared with the accuracy of rigid registration, the technology currently available to patients. This comparison allows investigation into whether biomechanical modeling provides good-quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 neurosurgery cases were warped onto their respective intraoperative configurations using both the biomechanics-based method and rigid registration. The Hausdorff distance-based evaluation process, which measures the difference between images, was used to quantify the performance of both registration methods. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p < 10(-4)). Even the modified hypothesis that fewer than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p = 0.02). The biomechanics-based method proved particularly effective in cases demonstrating large craniotomy-induced brain deformations. The outcome of this analysis suggests that nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theater as a possible means of improving neuronavigation and surgical outcomes.

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