Maurer C R, Fitzpatrick J M, Wang M Y, Galloway R L, Maciunas R J, Allen G S
Department of Computer Science and Neurological Surgery, Vanderbilt University, Nashville, TN 37235, USA.
IEEE Trans Med Imaging. 1997 Aug;16(4):447-62. doi: 10.1109/42.611354.
In this paper, we describe an extrinsic-point-based, interactive image-guided neurosurgical system designed at Vanderbilt University, Nashville, TN, as part of a collaborative effort among the Departments of Neurological Surgery, Computer Science, and Biomedical Engineering. Multimodal image-to-image (II) and image-to-physical (IP) registration is accomplished using implantable markers. Physical space tracking is accomplished with optical triangulation. We investigate the theoretical accuracy of point-based registration using numerical simulations, the experimental accuracy of our system using data obtained with a phantom, and the clinical accuracy of our system using data acquired in a prospective clinical trial by six neurosurgeons at four medical centers from 158 patients undergoing craniotomies to resect cerebral lesions. We can determine the position of our markers with an error of approximately 0.4 mm in X-ray computed tomography (CT) and magnetic resonance (MR) images and 0.3 mm in physical space. The theoretical registration error using four such markers distributed around the head in a configuration that is clinically practical is approximately 0.5-0.6 mm. The mean CT-physical registration error for the phantom experiments is 0.5 mm and for the clinical data obtained with rigid head fixation during scanning is 0.7 mm. The mean CT-MR registration error for the clinical data obtained without rigid head fixation during scanning is 1.4 mm, which is the highest mean error that we observed. These theoretical and experimental findings indicate that this system is an accurate navigational aid that can provide real-time feedback to the surgeon about anatomical structures encountered in the surgical field.
在本文中，我们描述了一种基于外部点的交互式图像引导神经外科系统，该系统由田纳西州纳什维尔的范德比尔特大学设计，是神经外科、计算机科学和生物医学工程系之间合作努力的一部分。使用可植入标记完成多模态图像到图像（II）和图像到物理（IP）配准。通过光学三角测量完成物理空间跟踪。我们使用数值模拟研究基于点的配准的理论精度，使用体模获得的数据研究我们系统的实验精度，并使用来自四个医疗中心的六位神经外科医生对158例接受开颅手术切除脑病变患者进行的前瞻性临床试验中获取的数据研究我们系统的临床精度。我们可以在X射线计算机断层扫描（CT）和磁共振（MR）图像中以约0.4毫米的误差确定标记的位置，在物理空间中以0.3毫米的误差确定标记的位置。使用四个在临床上实用的配置分布在头部周围的此类标记的理论配准误差约为0.5 - 0.6毫米。体模实验的平均CT - 物理配准误差为0.5毫米，扫描期间采用刚性头部固定获得的临床数据的平均CT - 物理配准误差为0.7毫米。扫描期间未采用刚性头部固定获得的临床数据的平均CT - MR配准误差为1.4毫米，这是我们观察到的最高平均误差。这些理论和实验结果表明，该系统是一种精确的导航辅助工具，可以向外科医生提供关于手术区域中遇到的解剖结构的实时反馈。
