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Automated brain shift correction using a pre-computed deformation atlas

Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems (MUIGS). In this paper, we use sparse intraoperative data acquired with the help of a laser-range scanner and introduce a strategy for integrating this information with the computational model. The model solutions are computed preoperatively and are combined with the help of a statistical model to predict the intraoperative brain shift. Validation of this approach is performed with measured intraoperative data. The results indicate our ability to predict intraoperative brain shift to an accuracy of 1.3mm ± 0.7mm. This method appears to be a promising technique for increasing the speed and accuracy of MUIGS.