Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

Abstract

Objective: The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.

Materials and methods: Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration.

Results: Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001).

Conclusions: We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

Figure 1

The current system for the image guided neurosurgery that integrates our novel non-rigid registration technology.

Figure 2

Two Dimensional View of Brain Shift. (a) Pre-operative Image. (b) Intra-operative Image (after the dura has been open and part of the tumor removed).

Figure 3

The architecture used to perform the non-rigid registration computations required by the neurosurgery. The data is transferred from the operating room (MRT) at our institution (BWH). The computations are performed during the case, and then are transferred back to BWH in the OR. The complete process takes approximately 5 minutes.

Figure 4

Meshes of the brain used to generate the biomechanical model.

Figure 5

Alignment of preoperative imaging is performed. Tumor segmentation is also carried out, prior the surgery. The images show the T1, DT-MRI tractography, and fMRI alignment together with the 3D reconstruction of the tumor. Neurosurgeons have complex information available to decide the best strategy to adopt for the craniotomy.

Figure 6

Non-rigid registration of preoperative imaging (T1, fMRI, DTI) with intraoperative imaging. We enhance the current procedure, by aligning preoperative imaging with intraoperative imaging. The DTI, fMRI, T1 images are displayed during tumor resection. Damage of critical structures can be avoided, while achieving gross tumor resection.

Figure 7

White matter fiber tracts deformation during the neurosurgery. The pre-operative data is shown in the images (a) and (b), while the intra-operative imaging is shown in (c) and (d). A significant displacement of the fiber tracts can be noticed.

Figure 8

Significant brain deformations require precise re-alignment of pre-operative fMRI and DTI datasets. (a) Illustrates the pre-operative images aligned (T1, fMRI) and several fiber tracts located in the vicinity of the tumor. Some of them are also crossing the tumor. Tumor is represented with green, while the fMRI in blue, and fiber tracts in magenta. (b) The fMRI is realigned with the intra-operative images, while compensating for the brain shift. The preoperative fiber tracts are also realigned and displayed during tumor resection (in yellow). (c) Shows the fMRI and fiber tracts before and after craniotomy. It is important to notice the deformation occurred.

Figure 9

Validation of the non-rigid registration alignment. (a) Non-rigid registered pre-operative T1 3T. (b) Intra-operative image T1 0.5T. (c) Contours extracted from (a) with the Canny edge detector. Contours extracted from (b) with the Canny edge detector. The accuracy of alignment is computed between the points on (c) and (d).

Figure 10

(a) 95% Hausdorff distance between the points on the edges of the registered image and intra-operative image. (b) presents the 95% Hausdorff between points on the edges on the rigid aligned preoperative image and the edges on the intra-operative data. The number of points on the extracted edges is different in the two cases, since typically MR 3T images have higher contrast.