Sun Kay, Pheiffer Thomas S, Simpson Amber L, Weis Jared A, Thompson Reid C, Miga Michael I
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
IEEE J Transl Eng Health Med. 2014 Apr 30;2. doi: 10.1109/JTEHM.2014.2327628.
Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patient's brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ~11-13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.
传统的影像引导神经外科手术依靠术前影像来提供手术导航信息和可视化。然而，一旦颅骨被打开且发生脑移位，这些影像就不再准确。为了应对由机械因素（如重力引起的变形）和生理效应（如高渗药物引起的萎缩或水肿引起的肿胀）导致的脑形状变化，必须及时向神经导航系统提供更新后的脑部影像，以便在手术室实际使用。在本文中，开发了一种新颖的术前和术中计算处理流程，用于在手术室中近乎实时地进行脑移位校正，以自动化和简化处理步骤。术前，从诊断图像体积生成患者脑部的计算机模型以及随后因手术导致的潜在变形图谱。在诊断和手术之间存在中期显著变化且需要重新成像的情况下，我们的术前流程可以在手术当天内生成。术中，使用光学跟踪的便携式激光测距仪收集测量皮质脑表面的稀疏数据。然后，这些数据被用于指导一个逆建模框架，通过该框架从预先计算的图谱解决方案中重建全脑体积变形，以快速匹配术中皮质表面移位测量值。一旦完成，体积位移场就用于更新，即变形，将术前脑部影像更新到术中移位状态。在本文中，针对计算流程和工作流程时间对五个手术病例进行了分析。关于皮质表面数据采集，近似执行时间为4.5分钟。包括扫描仪定位、数据采集、逆模型处理和图像变形在内的总更新过程约为11 - 13分钟。此外，还确定了易于实施的硬件、软件和工作流程，以便在不久的将来提高性能。
