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, w hich is essential for image guidance. A new approach to account for brain s hift, using computational methods driven by sparsely available operating ro om (OR) data, has been augmented with techniques for modeling tissue retrac tion and resection. METHODS: Modeling strategies to arbitrarily place and move an intracranial retractor and to excise designated tissue volumes have been implemented wit hin a computationally tractable framework. To illustrate these developments , a surgical case example, which uses OR data and the preoperative neuroana tomic image volume of the patient to generate a highly resolved, heterogene ous, 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 preo perative 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-ti me image-updating in the OR, Information regarding subsurface deformation a nd, in particular, changing tumor margins is presented. Some of the current limitations of the model, with respect to specific tissue mechanical respo nses, are highlighted. CONCLUSION: The results presented demonstrate that complex surgical events such as tissue retraction and resection can be incorporated intraoperativel y into the model-updating process for brain shift compensation in high-reso lution preoperative images.