TY - JOUR T1 - A Sparse Intraoperative Data-Driven Biomechanical Model to Compensate for Brain Shift during Neuronavigation JF - American Journal of Neuroradiology JO - Am. J. Neuroradiol. SP - 395 LP - 402 DO - 10.3174/ajnr.A2288 VL - 32 IS - 2 AU - D.-X Zhuang AU - Y.-X Liu AU - J.-S Wu AU - C.-J Yao AU - Y Mao AU - C.-X Zhang AU - M.-N Wang AU - W Wang AU - L.-F Zhou Y1 - 2011/02/01 UR - http://www.ajnr.org/content/32/2/395.abstract N2 - BACKGROUND AND PURPOSE: 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. MATERIALS AND 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. BCboundary conditionCGconjugate graduateFAflip angleFEfinite elementLRSlaser range scannerminminimumNRRnonrigid registrationPDEpartial differential equationPRFpatient reference frameRPMrobust point matching ER -