Image updating for brain deformation compensation in tumor resection

Xiaoyao Fan; Songbai Ji; Jonathan D. Olson; David W. Roberts; Alex Hartov; Keith D. Paulsen

doi:10.1117/12.2217223

18 March 2016 Image updating for brain deformation compensation in tumor resection

Xiaoyao Fan, Songbai Ji, Jonathan D. Olson, David W. Roberts, Alex Hartov, Keith D. Paulsen

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 97862A (2016) https://doi.org/10.1117/12.2217223
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Preoperative magnetic resonance images (pMR) are typically used for intraoperative guidance in image-guided neurosurgery, the accuracy of which can be significantly compromised by brain deformation. Biomechanical finite element models (FEM) have been developed to estimate whole-brain deformation and produce model-updated MR (uMR) that compensates for brain deformation at different surgical stages. Early stages of surgery, such as after craniotomy and after dural opening, have been well studied, whereas later stages after tumor resection begins remain challenging. In this paper, we present a method to simulate tumor resection by incorporating data from intraoperative stereovision (iSV). The amount of tissue resection was estimated from iSV using a "trial-and-error" approach, and the cortical shift was measured from iSV through a surface registration method using projected images and an optical flow (OF) motion tracking algorithm. The measured displacements were employed to drive the biomechanical brain deformation model, and the estimated whole-brain deformation was subsequently used to deform pMR and produce uMR. We illustrate the method using one patient example. The results show that the uMR aligned well with iSV and the overall misfit between model estimates and measured displacements was 1.46 mm. The overall computational time was ~5 min, including iSV image acquisition after resection, surface registration, modeling, and image warping, with minimal interruption to the surgical flow. Furthermore, we compare uMR against intraoperative MR (iMR) that was acquired following iSV acquisition.

Citation Download Citation

Xiaoyao Fan, Songbai Ji, Jonathan D. Olson, David W. Roberts, Alex Hartov, and Keith D. Paulsen "Image updating for brain deformation compensation in tumor resection", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97862A (18 March 2016); https://doi.org/10.1117/12.2217223

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