Modeling of retraction and resection for intraoperative updating of images.

Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.