Miga M I, Roberts D W, Kennedy F E, Platenik L A, Hartov A, Lunn K E, Paulsen K D
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA.
Neurosurgery. 2001 Jul;49(1):75-84; discussion 84-5. doi: 10.1097/00006123-200107000-00012.
Intraoperative tissue deformation that occurs during the course of neurosurgical procedures may compromise patient-to-image registration, which is essential for image guidance. A new approach to account for brain shift, using computational methods driven by sparsely available operating room (OR) data, has been augmented with techniques for modeling tissue retraction and resection.
Modeling strategies to arbitrarily place and move an intracranial retractor and to excise designated tissue volumes have been implemented within a computationally tractable framework. To illustrate these developments, a surgical case example, which uses OR data and the preoperative neuroanatomic image volume of the patient to generate a highly resolved, heterogeneous, finite-element model, is presented. Surgical procedures involving the retraction of tissue and the resection of a left frontoparietal tumor were simulated computationally, and the simulations were used to update the preoperative image volume to represent the dynamic OR environment.
Retraction and resection techniques are demonstrated to accurately reflect intraoperative events, thus providing an approach for near-real-time image-updating in the OR. Information regarding subsurface deformation and, in particular, changing tumor margins is presented. Some of the current limitations of the model, with respect to specific tissue mechanical responses, are highlighted.
The results presented demonstrate that complex surgical events such as tissue retraction and resection can be incorporated intraoperatively into the model-updating process for brain shift compensation in high-resolution preoperative images.
神经外科手术过程中发生的术中组织变形可能会影响患者与图像的配准，而这对于图像引导至关重要。一种考虑脑移位的新方法，利用由手术室（OR）稀疏可用数据驱动的计算方法，并已通过组织牵开和切除建模技术得到增强。
在一个易于计算的框架内实现了用于任意放置和移动颅内牵开器以及切除指定组织体积的建模策略。为说明这些进展，给出了一个手术病例示例，该示例使用手术室数据和患者的术前神经解剖图像体积来生成一个高分辨率、异质性的有限元模型。对涉及组织牵开和左额顶叶肿瘤切除的手术过程进行了计算模拟，并利用这些模拟来更新术前图像体积以代表动态的手术室环境。
牵开和切除技术被证明能准确反映术中事件，从而为手术室中的近实时图像更新提供了一种方法。展示了关于表面下变形，特别是不断变化的肿瘤边缘信息。强调了该模型在特定组织力学反应方面当前的一些局限性。
所呈现的结果表明，诸如组织牵开和切除等复杂手术事件可在术中纳入模型更新过程，以补偿高分辨率术前图像中的脑移位。
