Fusion and visualization of intraoperative cortical images with preoperative models for epilepsy surgical planning and guidance.

OBJECTIVE

During epilepsy surgery it is important for the surgeon to correlate the preoperative cortical morphology (from preoperative images) with the intraoperative environment. Augmented Reality (AR) provides a solution for combining the real environment with virtual models. However, AR usually requires the use of specialized displays, and its effectiveness in the surgery still needs to be evaluated. The objective of this research was to develop an alternative approach to provide enhanced visualization by fusing a direct (photographic) view of the surgical field with the 3D patient model during image guided epilepsy surgery.

MATERIALS AND METHODS

We correlated the preoperative plan with the intraoperative surgical scene, first by a manual landmark-based registration and then by an intensity-based perspective 3D-2D registration for camera pose estimation. The 2D photographic image was then texture-mapped onto the 3D preoperative model using the solved camera pose. In the proposed method, we employ direct volume rendering to obtain a perspective view of the brain image using GPU-accelerated ray-casting. The algorithm was validated by a phantom study and also in the clinical environment with a neuronavigation system.

RESULTS

In the phantom experiment, the 3D Mean Registration Error (MRE) was 2.43 ± 0.32 mm with a success rate of 100%. In the clinical experiment, the 3D MRE was 5.15 ± 0.49 mm with 2D in-plane error of 3.30 ± 1.41 mm. A clinical application of our fusion method for enhanced and augmented visualization for integrated image and functional guidance during neurosurgery is also presented.

CONCLUSIONS

This paper presents an alternative approach to a sophisticated AR environment for assisting in epilepsy surgery, whereby a real intraoperative scene is mapped onto the surface model of the brain. In contrast to the AR approach, this method needs no specialized display equipment. Moreover, it requires minimal changes to existing systems and workflow, and is therefore well suited to the OR environment. In the phantom and in vivo clinical experiments, we demonstrate that the fusion method can achieve a level of accuracy sufficient for the requirements of epilepsy surgery.