Image-guided neuro navigation systems are still reliant on the rigid alignment of preoperative tomographic imaging information (usually magnetic resonance imaging data) to the intraoperative patient anatomy. It is well understood that soft tissue deformations during surgery can compromise that alignment leading to significant discrepancies between imaged neuroanatomy and its physical space counterpart. While intraoperative MRI/CT are available, the encumbrance, cost, and workflow have inhibited the widespread adoption as a standard of care. As a result, computational imaging efforts to adjust for deformations have been under investigation. The goal of this work was to perform a feasibility study to evaluate a model-based strategy to correct for deformations and evaluate in real-time. For this study n=8 subjects were enrolled in an IRB approved study at Vanderbilt University Medical Center. Model-based deformation correction was performed and evaluated for six of the eight surgeries with a total of seven evaluations performed (in one case, both the attending and resident evaluated the correction separately). With respect to evaluation, at the end of each surgery, the model-deformed images were displayed next to the gold standard preoperative images in the operating room. A stylus was used by the surgeon to interrogate the surgical field and evaluate the alignments between the model-based corrected guidance system and standard-of-care conventional IGS system. For all cases with substantial shift, which was six of the seven evaluations, surgeons preferred the model-based approach in terms of image alignment to patient anatomy. For the case with minimal shift, the surgeon found no difference between the two systems. For six of the seven evaluations, surgeons had an overall preference for the model-based approach. In conclusion, we demonstrated initial feasibility of using a model-based deformation correction scheme during brain tumor resection surgeries. Additionally, based on the surgeon consensus of improved image alignment to intraoperative anatomy, this study demonstrates the potential benefit of our approach in terms of evaluating resection boundaries intraoperatively.
The quality of neurosurgical planning can become compromised by soft tissue deformations that occur during surgery (i.e., brain shift). Conventional image guidance systems do not consider these intraoperative changes. In recent efforts, model based strategies have been developed to estimate surgical load displacements and modify the patient’s data intraoperatively to account for brain shift. While the efficacy of the model has been previously established, there is also an opportunity to further assist surgical planning with this pipeline. To address this, a mobile application designed for an Android tablet was developed to display simulated brain shifts that would occur during brain tumor surgery. The application has two primary functions to facilitate planning: a patient positioning mode and a simulation mode. The patient positioning mode allows the neurosurgeon to load the patient’s preoperative MR data and create a surgical plan (i.e., head orientation and craniotomy location) for the procedure. The simulation mode then displays both the preoperative data and the model predicted brain shift as a function of the specified orientation from the patient positioning mode. Additionally, to account for positional variations between planning and procedural implementation, the simulation mode also displays solutions with additional perturbations to the planned positioning to estimate shift possibilities. To assess the simulation mode prototype, practicing neurosurgeons were provided a prototype demonstration and interviews were performed to evaluate efficacy and design. Due to computational rendering and 3D rotation shortcomings based on clinical feedback, the prototype was redesigned into a full mixed reality simulation app on the Microsoft HoloLens. Preliminary survey responses show that the prototype could be an impactful surgical planning tool, especially among neurosurgeons with less experience.
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