A Fast and Efficient Method to Compensate for Brain Shift for Tumor Resection Therapies Measured Between Preoperative and Postoperative Tomograms

Abstract

In this paper, an efficient paradigm is presented to correct for brain shift during tumor resection therapies. For this study, high resolution pre-operative (pre-op) and post-operative (post-op) magnetic resonance images were acquired for 8 in vivo patients, and surface/subsurface shift was identified by manual identification of homologous points between the pre-op and immediate postop tomograms. Cortical surface deformation data was then used to drive an inverse problem framework. The manually identified subsurface deformations served as a comparison towards validation. The proposed framework recaptured 85% of the mean sub-surface shift. This translated to a sub-surface shift error of 0.4mm±0.4mm for a measured shift of 3.1mm±0.6mm. The patient's pre-op tomograms were also deformed volumetrically using displacements predicted by the model. Results presented allow a preliminary evaluation of correction both quantitatively, and visually. While intra-op MR imaging data would be optimal, the extent of shift measured from pre-to postoperative MR was comparable to clinical conditions. This study demonstrates the accuracy of the proposed framework in predicting full volume displacements from sparse shift measurements. It also shows that the proposed framework can be extended and used to update pre-op images on a time scale that is compatible with surgery.

Evaluation of Conoscopic Holography for Estimating Tumor Resection Cavities in Model-Based Image-Guided Neurosurgery

“…This could be explained by the early timing of the postoperative scans (all scans were acquired within 25 h of surgery) (see Table IV). In some respects, this does lend credence to our previous investigations [8] where the correction approach was driven by cortical surface points extracted from pre- and postoperative MRI points and validated against subsurface corresponding target points.…”

“…The pipeline of tasks in this process from the preoperative stages of mesh generation and boundary condition assignment to intraoperative data acquisition and image update has been reported in other work [4], [8], [9] and are summarized in the Appendix. While this framework is potentially powerful, the ability to characterize a resection cavity with a low-encumbrance device like the conoprobe during surgery may potentially inform this framework.…”

“…As an alternative to whole-brain intraoperative imaging, one approach uses low-cost readily available intraoperative data integrated into a framework called model-updated image-guided neurosurgery (MUIGNS) [29]. This framework combines the fidelity of computational models in conjunction with intraoperative data acquisition (by way of laser-range scanning (LRS) [3], [4], [8], [9], ultrasonography [17], [18], [29], or stereo reconstruction [29], [35]) as a means for updating high-resolution preoperative-based images to reflect current operative conditions. In this paradigm, intraoperative data that characterize brain deformation (i.e., cortical shift measurements) are used to formulate an inverse problem framework that uses patient-specific biomechanical models.…”

“…Much of the related work in MUIGNS has been focused on the development of computational models for brain deformation to include methods of retraction and the incorporation of patient specific properties and anatomical constraints [3], [6], [8], [9], [17], [18], [21], [23], [29], [35]. A critical challenge in this work is the intraoperative patient data to drive these models.…”

A method for the assessment of time-varying brain shift during navigated epilepsy surgery

“…It has also been shown that local volumetric brain deformation can be inferred given the cortical surface displacement [34]. In this context, inside the framework of the European project ACTIVE, a series of high-resolution model deformations based on brain constitutive law and complex loading conditions, such as brain shift due to gravity, organ volume changes due to drug reactions or due to edema, were developed.…”