1 McGill University, Montreal Neurological Institute and Hospital, Department of Biomedical Engineering, Montreal, Québec, Canada.
2 Concordia University, PERFORM Centre, Department of Computer Science and Software Engineering, Montreal, Québec, Canada.
3 McGill University, Montreal Neurological Institute and Hospital, Department of Neurology and Neurosurgery, Montreal, Québec, Canada.
4 McGill University, Centre for Intelligent Machines, Department of Electrical and Computer Engineering, Montreal, Québec, Canada.

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases.

J Med Imaging (Bellingham). 2018 Apr;5(2):021210

Authors: Gerard IJ, Kersten-Oertel M, Drouin S, Hall JA, Petrecca K, De Nigris D, Di Giovanni DA, Arbel T, Collins DL

Abstract

We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient's cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

PMID: 29392162 [PubMed]