Gerard Ian J, Kersten-Oertel Marta, Drouin Simon, Hall Jeffery A, Petrecca Kevin, De Nigris Dante, Di Giovanni Daniel A, Arbel Tal, Collins D Louis
McGill University, Montreal Neurological Institute and Hospital, Department of Biomedical Engineering, Montreal, Québec, Canada.
Concordia University, PERFORM Centre, Department of Computer Science and Software Engineering, Montreal, Québec, Canada.
J Med Imaging (Bellingham). 2018 Apr;5(2):021210. doi: 10.1117/1.JMI.5.2.021210. Epub 2018 Jan 26.
We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient's cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.
我们展示了我们的工作,该工作研究了在脑肿瘤的图像引导神经外科手术(IGNS)中,将术中超声用于脑移位校正与增强现实(AR)可视化相结合以对患者特定模型进行术中解读的可行性。我们将两种成像技术结合用于图像引导的脑肿瘤神经外科手术。在整个手术过程中,使用AR通过患者皮层、血管系统和病变的三维(3-D)患者特定模型来评估不同的手术策略。术中获取超声图像,并将术前图像和模型与术中数据进行配准。使用定性和定量指标评估AR视图的质量和可靠性。对八名患者的初步研究证明了这两种技术的可行组合及其互补特性。在每种情况下,AR可视化都能使外科医生在手术的较长时间内准确地可视化感兴趣的解剖结构和病理情况。所有病例中与配准错误、脑移位和AR相关的不准确情况均得到改善。这些结果表明,将基于超声的配准与AR相结合有潜力成为神经外科医生的有用工具,通过增进对复杂3-D医学成像数据的理解并延长IGNS的可靠使用时间来改善术中针对患者的规划。