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Preoperative magnetic resonance images are typically used for neuronavigation in image-guided neurosurgery. However,
intraoperative brain deformation (e.g., as a result of gravitation, loss of cerebrospinal fluid, retraction, resection, etc.)
significantly degrades the accuracy in image guidance, and must be compensated for in order to maintain sufficient
accuracy for navigation. Biomechanical finite element models are effective techniques that assimilate intraoperative data
and compute whole-brain deformation from which to generate model-updated MR images (uMR) to improve accuracy in
intraoperative guidance. To date, most studies have focused on early surgical stages (i.e., after craniotomy and
durotomy), whereas simulation of more complex events at later surgical stages has remained to be a challenge using
biomechanical models. We have developed a method to simulate partial or complete tumor resection that incorporates
intraoperative volumetric ultrasound (US) and stereovision (SV), and the resulting whole-brain deformation was used to
generate uMR. The 3D ultrasound and stereovision systems are complimentary to each other because they capture
features deeper in the brain beneath the craniotomy and at the exposed cortical surface, respectively. In this paper, we
illustrate the application of the proposed method to simulate brain tumor resection at three temporally distinct surgical
stages throughout a clinical surgery case using sparse displacement data obtained from both the US and SV systems. We
demonstrate that our technique is feasible to produce uMR that agrees well with intraoperative US and SV images after
dural opening, after partial tumor resection, and after complete tumor resection. Currently, the computational cost to
simulate tumor resection can be up to 30 min because of the need for re-meshing and the trial-and-error approach to
refine the amount of tissue resection. However, this approach introduces minimal interruption to the surgical workflow,
which suggests the potential for its clinical application with further improvement in computational efficiency.
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