Simpson Amber L, Sun Kay, Pheiffer Thomas S, Rucker D Caleb, Sills Allen K, Thompson Reid C, Miga Michael I
IEEE Trans Biomed Eng. 2014 Jun;61(6):1833-43. doi: 10.1109/TBME.2014.2308299.
Surgical navigation relies on accurately mapping the intraoperative state of the patient to models derived from preoperative images. In image-guided neurosurgery, soft tissue deformations are common and have been shown to compromise the accuracy of guidance systems. In lieu of whole-brain intraoperative imaging, some advocate the use of intraoperatively acquired sparse data from laser-range scans, ultrasound imaging, or stereo reconstruction coupled with a computational model to drive subsurface deformations. Some authors have reported on compensating for brain sag, swelling, retraction, and the application of pharmaceuticals such as mannitol with these models. To date, strategies for modeling tissue resection have been limited. In this paper, we report our experiences with a novel digitization approach, called a conoprobe, to document tissue resection cavities and assess the impact of resection on model-based guidance systems. Specifically, the conoprobe was used to digitize the interior of the resection cavity during eight brain tumor resection surgeries and then compared against model prediction results of tumor locations. We should note that no effort was made to incorporate resection into the model but rather the objective was to determine if measurement was possible to study the impact on modeling tissue resection. In addition, the digitized resection cavity was compared with early postoperative MRI scans to determine whether these scans can further inform tissue resection. The results demonstrate benefit in model correction despite not having resection explicitly modeled. However, results also indicate the challenge that resection provides for model-correction approaches. With respect to the digitization technology, it is clear that the conoprobe provides important real-time data regarding resection and adds another dimension to our noncontact instrumentation framework for soft-tissue deformation compensation in guidance systems.
手术导航依赖于将患者的术中状态准确映射到从术前图像派生的模型上。在图像引导的神经外科手术中，软组织变形很常见，并且已证明会损害引导系统的准确性。由于缺乏全脑术中成像，一些人主张使用术中从激光测距扫描、超声成像或立体重建获取的稀疏数据，再结合计算模型来驱动皮下变形。一些作者报告了使用这些模型来补偿脑下垂、肿胀、回缩以及应用甘露醇等药物的情况。迄今为止，用于对组织切除进行建模的策略一直很有限。在本文中，我们报告了一种名为圆锥探头的新型数字化方法的应用经验，该方法用于记录组织切除腔并评估切除对基于模型的引导系统的影响。具体而言，在八例脑肿瘤切除手术中，使用圆锥探头对切除腔内部进行数字化处理，然后与肿瘤位置的模型预测结果进行比较。我们需要注意的是，并未尝试将切除纳入模型，而是旨在确定是否能够进行测量以研究对组织切除建模的影响。此外，将数字化的切除腔与术后早期的磁共振成像扫描进行比较，以确定这些扫描是否能进一步为组织切除提供信息。结果表明，尽管没有明确对切除进行建模，但在模型校正方面仍有好处。然而，结果也表明切除给模型校正方法带来了挑战。关于数字化技术，很明显圆锥探头提供了有关切除的重要实时数据，并为我们用于引导系统中软组织变形补偿的非接触式仪器框架增添了新的维度。
