Wang A, Mirsattari S M, Parrent A G, Peters T M
Imaging Research Laboratories, Robarts Research Institute , London, Ontario.
Comput Aided Surg. 2011;16(4):149-60. doi: 10.3109/10929088.2011.585805. Epub 2011 Jun 13.
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.
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.
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.
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.
在癫痫手术中，外科医生将术前皮质形态（来自术前图像）与术中环境相关联非常重要。增强现实（AR）为将真实环境与虚拟模型相结合提供了一种解决方案。然而，AR通常需要使用专门的显示器，其在手术中的有效性仍有待评估。本研究的目的是开发一种替代方法，通过在图像引导的癫痫手术中将手术视野的直接（摄影）视图与三维患者模型相融合来提供增强的可视化效果。
我们首先通过基于手动标记的配准，然后通过基于强度的透视三维-二维配准来估计相机姿态，从而将术前计划与术中手术场景相关联。然后，使用求解出的相机姿态将二维摄影图像纹理映射到三维术前模型上。在所提出的方法中，我们采用直接体绘制，通过图形处理器（GPU）加速光线投射来获得脑图像的透视图。该算法通过模型研究以及在临床环境中使用神经导航系统进行了验证。
在模型实验中，三维平均配准误差（MRE）为2.43±0.32毫米，成功率为100%。在临床实验中，三维MRE为5.15±0.49毫米，二维平面内误差为3.30±1.41毫米。还介绍了我们的融合方法在神经外科手术中用于增强和扩展可视化以进行综合图像和功能引导的临床应用。
本文提出了一种替代复杂AR环境的方法来辅助癫痫手术，即将真实的术中场景映射到脑表面模型上。与AR方法相比，该方法不需要专门的显示设备。此外，它对现有系统和工作流程的改动极小，因此非常适合手术室环境。在模型和体内临床实验中，我们证明融合方法能够达到足以满足癫痫手术要求的精度水平。
