Since the introduction of the first intraoperative frameless stereotactic navigation device, image guided neurosurgery has become an essential tool for many neurosurgical procedures due to its ability to minimize surgical trauma by allowing for the precise localization of surgical targets. The integration of preoperative image information into a comprehensive patient-specific model enables surgeons to preoperatively evaluate the risks involved and define the most appropriate surgical strategy. Perhaps more importantly, such systems enable surgery of previously inoperable cases by helping to locate safe surgical corridors through IGNS-identified non-critical areas.

For intraoperative use, neuronavigation systems must relate the physical location of a patient with the preoperative models by means of a transformation that relates the two through a patient-to-image mapping. Throughout the intervention, hardware movement, an imperfect patient-image mapping, and movement of brain tissue during surgery invalidates the patient-to-image mapping. These sources of inaccuracy, collectivey described as ‘brain shift’, reduce the effectiveness of using preoperative patient specific models intraoperatively. Intraoperative imaging, such as MRI, has been shown to improve the accuracy of tumour resections through lengthened image guidance. However, such technology is extremely expensive, prolongs surgery, poses logistical challenges during awake surgeries, and is available in only a few centres worldwide. We have developed a neuronavigation platform (IBIS Neuronav) that integrates tissue deformation tracking during surgery based on tracked intraoperative ultrasound (iUS) that can accurately align all pre-operative data to the iUS to account for brain shift throughout a surgical intervention.

 

 

Reference:

[1] I. Gerard and D. L. Collins, “An Analysis of Tracking Error in Image Guided Neurosurgery”, Int. J. Computer Assisted Radiolgy and Surgery. 2015, Jan 4; 1–10 [Epub ahead of print].

[2] H. Rivaz, D.L. Collins, “Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery”, Ultrasound in Medicine and Biology. 2015 Feb; 41(2): 574–587.

[3] H. Rivaz, S.J.S Chen, D.L. Collins, “Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery”, IEEE Transactions on Medical Imaging. 2015 Feb; 34(2); 366–380.

[4] H. Rivas, Z. Karimaghaloo, D.L. Collins, “Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information”, IEEE Trans Med Imag. 2014 Mar;33(3):708–25.

[5] S. Beriault, A. Sadikot, F. Alsubaie, S. Drouin, D.L. Collins, G.B. Pike. “Neuronavigation using susceptibility-weighted venography: application to deep brain stimulation and comparison with gadolinium contrast”, Journal of Neurosurgery. 2014 Jul;121(1):131–41.

[6] L. Mercier, D Araujo, C Haegelen, RF Del Maestro, K Petrecca, DL Collins, “Registering pre- and post-resection 3D ultrasound for improved residual brain tumor localization”, Ultrasound in Medicine and Biology, 2013 Jan;39(1):16–29.

[7] M. Kersten-Oertel, P. Jannin, D.L. Collins, “The State of the Art in Mixed Reality Visualization in Image-Guided Surgery”, IEEE Transactions on Visualization and Computer Graphics. 2013 Mar;37(2):98–112.

[8] D. De Nigris, D. L. Collins, T. Arbel, “ Fast Rigid Registration of Pre-Operative Magnetic Resonance Images to Intra-Operative Ultrasound for Neurosurgery based on High Confidence Gradient Orientations”, 2013 July; 8(4): 649–661.