Estimation of brain deformation for volumetric image updating in protoporphyrin IX fluorescence-guided resection

Abstract

Introduction: Fluorescence-guided resection (FGR) of brain tumors is an intuitive, practical and emerging technology for visually delineating neoplastic tissue exposed intraoperatively. Image guidance is the standard technique for producing 3-dimensional spatially coregistered information for surgical decision making. Both technologies together are synergistic: the former detects surface fluorescence as a biomarker of the current surgical margin while the latter shows coregistered volumetric neuroanatomy but can be degraded by intraoperative brain shift. We present the implementation of deformation modeling for brain shift compensation in protoporphyrin IX FGR, integrating these two sources of information for maximum surgical benefit.

Methods: Two patients underwent FGR coregistered with conventional image guidance. Histopathological analysis, intraoperative fluorescence and image space coordinates were recorded for biopsy specimens acquired during surgery. A biomechanical brain deformation model driven by intraoperative ultrasound data was used to generate updated MR images.

Results: Combined use of fluorescence signatures and updated MR image information showed substantially improved accuracy compared to fluorescence or the original (i.e., nonupdated) MR images, detecting only true positives and true negatives, and no instances of false positives or false negatives.

Conclusion: Implementation of brain deformation modeling in FGR shows promise for increasing the accuracy of neurosurgical guidance in the delineation and resection of brain tumors.

Fig. 1.

Preoperative, updated, and intraoperative images of patient 1. The top row from left to right (a–c) shows preoperative T₁-weighted postdurotomy MR images without compensation for brain shift (pMR). The bottom row from left to right (e–g) shows the updated T₁-weighted MR images immediately after durotomy with compensation for brain shift (uMR). The surgical microscope was coregistered and tracked by the navigational system. The white light (d) and blue light (h) images corresponding to the tracked coordinates of the microscope focal point in image space (crosses) are shown. A bulging, highly fluorescent tumor tissue confirmed by pathology as neoplastic tissue corresponds to an area of contrast enhancement in the uMR. Compare this area of fluorescent brain tissue to the pMR, which inaccurately points to the scalp region.

Fig. 2.

Test results for biopsy specimens. Percentage of biopsy specimens (n = 18) for FGR, IGWO, IGW, and FGR-IGW that were true negative (TN), false negative (FN), and true positive (TP) are shown for both cases. No false positives were recorded in this study.

Fig. 3.

Gadolinium-enhanced preoperative and postoperative T₁-weighted MR images. Preoperative images for patients 1 (a) and 2 (d). Postoperative images for patients 1 (b) and 2 (e). Postoperative subtraction images for patients 1 (c) and 2 (f).

Fig. 4.

Cross-sectional images of intraoperative 3-dimensional US from patient 1. US image before (a) and after (b) dura opening. Overlays of US images before (red) and after (green) dura opening (regions appearing in yellow align) are shown in c and d. Apparent misalignment due to brain shift is evident in c; after both rigid and nonrigid registration with computed displacement vectors (white arrows) is presented in d.

Fig. 5.

Schema of the brain deformation model. Model preprocessing: the brain is segmented from the pMR image to generate triangular surface and tetrahedral volumetric meshes, and appropriate boundary conditions are assigned. All preprocessing steps are performed prior to surgery. Intraoperative data: tracked 3-dimensional US images are acquired before and after durotomy, and are spatially merged with the patient's head and then re-registered with the pMR image. Displacement data are computed from registered pre- and postdurotomy US images to drive a biomechanical model. Biomechanical model: whole-brain deformation is computed based on boundary conditions assigned and intraoperative sparse data provided to generate an uMR image volume.