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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.
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