Image updating for brain deformation compensation: cross-validation with intraoperative ultrasound

摘要

Intraoperative image guidance using preoperative MR images (pMR) is widely used in neurosurgery, but the accuracy can be compromised by brain deformation as soon as the dura is open. Biomechanical finite element models (FEM) have been developed to compensate for brain deformation that occurs at different surgical stages. Intraoperative sparse data extracted from the exposed cortical surface and/or from deeper brain is used to drive the FEM model to compute whole-brain deformation field and produce model-updated MR (uMR) that matches the surgical scene. In previous studies, we quantified the accuracy using model-data misfit (i.e., the root-mean-square error between model estimates and sparse data), as well as target registration errors (TRE) of surface features (such as vessel junctions), and showed that the accuracy on the cortical surface was .1-2 mm. However, the accuracy in deeper brain has not been investigated, as it is challenging to obtain subsurface features during surgery for accuracy assessment. In this study, we used intraoperative stereovision (iSV) to extract sparse data, which was employed to drive the FEM model and produce uMR, and acquired co-registered intraoperative ultrasound images (iUS) at different surgical stages in 2 cases for cross validation. We quantify model-data misfit, and compare model updated MR with iUS for qualitative assessment of accuracy in deeper brain. The results show that the model-data misfit was 2.39 and 0.64 mm, respectively, for the 2 cases reported, and uMR aligned well with both iSV and iUS, indicating a good accuracy both on the surface and in deeper brain.