title: A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation.
abstract: OBJECTIVE
  Intraoperative brain deformation is an important factor compromising the accuracy of image-guided neurosurgery. The purpose of this study was to elucidate the role of a model-updated image in the compensation of intraoperative brain shift.
  
  
  METHODS
  An FE linear elastic model was built and evaluated in 11 patients with craniotomies. To build this model, we provided a novel model-guided segmentation algorithm. After craniotomy, the sparse intraoperative data (the deformed cortical surface) were tracked by a 3D LRS. The surface deformation, calculated by an extended RPM algorithm, was applied on the FE model as a boundary condition to estimate the entire brain shift. The compensation accuracy of this model was validated by the real-time image data of brain deformation acquired by intraoperative MR imaging.
  
  
  RESULTS
  The prediction error of this model ranged from 1.29 to 1.91 mm (mean, 1.62 ± 0.22 mm), and the compensation accuracy ranged from 62.8% to 81.4% (mean, 69.2 ± 5.3%). The compensation accuracy on the displacement of subcortical structures was higher than that of deep structures (71.3 ± 6.1%:66.8 ± 5.0%, P < .01). In addition, the compensation accuracy in the group with a horizontal bone window was higher than that in the group with a nonhorizontal bone window (72.0 ± 5.3%:65.7 ± 2.9%, P < .05).
  
  
  CONCLUSIONS
  Combined with our novel model-guided segmentation and extended RPM algorithms, this sparse data-driven biomechanical model is expected to be a reliable, efficient, and convenient approach for compensation of intraoperative brain shift in image-guided surgery.
authors.affiliation: Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai Neurosurgical Center, PR China.
authors.forename: D-X
authors.initials: DX
authors.lastname: Zhuang
authors.forename: Y-X
authors.initials: YX
authors.lastname: Liu
authors.forename: J-S
authors.initials: JS
authors.lastname: Wu
authors.forename: C-J
authors.initials: CJ
authors.lastname: Yao
authors.forename: Y
authors.initials: Y
authors.lastname: Mao
authors.forename: C-X
authors.initials: CX
authors.lastname: Zhang
authors.forename: M-N
authors.initials: MN
authors.lastname: Wang
authors.forename: W
authors.initials: W
authors.lastname: Wang
authors.forename: L-F
authors.initials: LF
authors.lastname: Zhou
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001696:Biomechanical Phenomena; D001921:Brain / Q000502:physiology* / Q000601:surgery*; D002648:Child; D003399:Craniotomy; D004548:Elasticity; D005260:Female; D006801:Humans; D007432:Intraoperative Period; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D038361:Neuronavigation; D055815:Young Adult