In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in eloquent areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This talk reports the results (using clinical data) of a comparison of the accuracy and performance among four open-source non-rigid registration methods for handling brain deformation in the presence of tumor resection, including a new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby automatically handling deformation in the presence of resection.
In this talk data from 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. Three measures aid in assessing the accuracy of the registration methods: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Performance analysis showed that the adaptive method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting and significantly better than other readily-available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room.
Given time availability a brief description of related real-time Image-to-Mesh conversion technologies at developed CRTC to facility adaptive method will be presented.