@article {Zhuang395, author = {D.-X Zhuang and Y.-X Liu and J.-S Wu and C.-J Yao and Y Mao and C.-X Zhang and M.-N Wang and W Wang and L.-F Zhou}, title = {A Sparse Intraoperative Data-Driven Biomechanical Model to Compensate for Brain Shift during Neuronavigation}, volume = {32}, number = {2}, pages = {395--402}, year = {2011}, doi = {10.3174/ajnr.A2288}, publisher = {American Journal of Neuroradiology}, abstract = {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 {\textpm} 0.22 mm), and the compensation accuracy ranged from 62.8\% to 81.4\% (mean, 69.2 {\textpm} 5.3\%). The compensation accuracy on the displacement of subcortical structures was higher than that of deep structures (71.3 {\textpm} 6.1\%:66.8 {\textpm} 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 {\textpm} 5.3\%:65.7 {\textpm} 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}, issn = {0195-6108}, URL = {https://www.ajnr.org/content/32/2/395}, eprint = {https://www.ajnr.org/content/32/2/395.full.pdf}, journal = {American Journal of Neuroradiology} }