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Visualization and Planning of Neurosurgical Interventions with Straight Access

IPCAI: 2010. Visualization and Planning of Neurosurgical Interventions with Straight Access. Nikhil V. Navkar 1,2 , Nikolaos V. Tsekos 1 , Jason R. Stafford 3 , Jeffrey S. Weinberg 3 , and Zhigang Deng 2. 1 Medical Robotics Lab, University of Houston

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Visualization and Planning of Neurosurgical Interventions with Straight Access

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  1. IPCAI: 2010 Visualization and Planning of Neurosurgical Interventions with Straight Access Nikhil V. Navkar1,2, Nikolaos V. Tsekos1, Jason R. Stafford3, Jeffrey S. Weinberg3, and Zhigang Deng2 1 Medical Robotics Lab, University of Houston 2 Computer Graphics and Interactive Media Lab, University of Houston 3 M.D. Anderson Cancer Center, University of Texas, Houston

  2. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. INTRODUCTION: The work proposes a technique for simple and efficient visualization of the region of intervention for neurosurgical procedures.

  3. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. INTRODUCTION: • The large volume of three dimensional brain data from different imaging modalities pose a major challenge either at the preoperative or the intraoperative stage of an image-guided neurosurgical interventional procedure. • It is difficult to visualize, comprehend and manipulate the data at the same time.

  4. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. INTRODUCTION: • This work proposes an approach based on the generation of Access Maps on the surface of the region of intervention. • Incorporate the information of underlying tissue and visualize it on the outer surface of the patient. • Offers an intuitive way for a neurosurgeon to quantitatively select the optimal path of access for a given target anatomy.

  5. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: • In this work the Access Maps consists of : • Direct Impact Map • Proximity Map • Path Length Map

  6. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Preprocessing of data: Head Surface 3D Model Generation Marching cube . Laplace+HC mesh smoothing followed by a low pass filter. MRI Data Acquisition T2 Weighted MRI Acquisition matrix 256 X 256 Number of slices = 144 Pixel size of 1.0 x 1.0 mm Interslice distance = 1.0 mm Segmentation of Images Thresholding to segment the region outside the head (like a negative mold) and then applying an inversion filter to get the inside region.

  7. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Preprocessing of data: Brain Vascular MRI Data Acquisition Time-of-flight (TOF) MRA Acquisition matrix 768 X 576 Number of slices = 136 Pixel size of 0.3 x 0.3 mm Interslice distance = 0.6 mm 3D Model Generation Marching cube. Mesh smoothing by a low pass filter. Segmentation of Images: Thresholding and applying connectivity filter based on region growing by selecting the base of the vessel as the seed point.

  8. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Overview of Processes: Applying Threshold Normalized Value of Path Length Map Direct Impact Map Preprocessed Data Access Maps Normalized Value of Proximity Map Applying Threshold

  9. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Direct Impact Map : • Generated by the concept of Ray Casting. • Projects the shadow of the vital structures on the skin by considering the target as a point source of light. • Avoids direct impact on the interventional tool on the vital structure.

  10. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Direct Impact Map : If any insertion is made through a point on the map it would directly impact the vital tissue (vessels in the above case).

  11. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Proximity Map : • Generated by projecting a safe three dimension virtual buffer space encapsulating the vital structures. • Properties of intervention tool such as deflection, thickness may bring the interventional tool to very close proximity and even puncture the vital tissue.

  12. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Proximity Map : For a given insertion point v on the head surface PR(v) (mm) is the shortest distance between the insertion path and the vital structure.

  13. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Proximity Map : 11.17 mm 0 mm Shows the minimum distance (in mm) which the interventional tool maintains from the vital structure.

  14. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Path Length Map : • Shorter the distance travel by interventional tool, the less is the risk of trauma (even to non-vital structures). • Projects the depth of the target from the surface of intervention.

  15. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. METHODS: Path Length Map : 132.30 mm 37.21 mm PL(v) is the distance between the given insertion point v and the target.

  16. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. RESULTS: IP2 PR(IP2)=4.00mm 37.99mm Target (-30,0,0) IP2 (-64.76, 2.25, 15.15) IP1 (-30.61,-2.31,59.59) IP1 PR(IP1)=0.00mm 39.67mm 37.99mm IP3 PR(IP3)=4.74mm IP3 (-64.06,20.36,0.85) Thresholds: PL(v) = 40.26 mm (Path Length Threshold) PR(v) = 4.00 mm (Proximity Threshold) IP3 is the optimal insertion point for given threshold.

  17. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. RESULTS: The camera is placed on the tip of an interventional tool and follows the selected path of insertion.

  18. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. RESULTS: Preliminary evaluation of the effectiveness of the described access maps for different neurosurgical interventional procedures.

  19. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. CONCLUSION: The described Access Maps is an intuitive and simple approach for visualizing 3D multimodal information about the anatomy of the region of intervention and safety of selected insertion paths.

  20. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. ACKNOWLEDGMENTS: • This work was supported in part by: • NSF CNS-0932272 • NSF IIS-0914965 • Texas NHARP 003652-0058-2007 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

  21. IPCAI 2010: Visualization and Planning of Neurosurgical Interventions with Straight Access. REFERENCES: Path planning for reducing tissue damage in minimally invasive brain access. Popovic, A., and Trovato, K. In Computer Assisted Radiology and Surgery supplemental (2009). An intelligent atlas-based planning system for keyhole neurosurgery. Tirelli, P., De Momi, E., Borghese, N. A., and Ferrigno, G. Int’l J. of Computer Assisted Radiology and Surgery (2009) Automatic Tra jectory Planning for Deep Brain Stimulation: A Feasibility Study. Brunenberg, E.J.L., Bartroli, A.V., Vandewalle, V.V., Temel, Y., Ackermans, L., Platel, B., Romenij, B.M.H. In MICCAI (2007) Neuropath planner-automatic path searching for neurosurgery. Fujii, T., Emoto, H., Sugou, N., Mito, T. Shibata, I. In Proc. of Computer Assisted Radiology and Surgery (2003). A 3-D visualization method for image-guided brain surgery. Bourbakis, N.G., Awad, M. IEEE Trans. on Systems, Man, Cybernetics (2003) Multimodal and Multi-Informational Neuronavigation. Seigneuret, J.F., Jannin, P., Fleig, O.J., Seigneuret, E., Mor, X., Raimbault, M., Cedex, R. In Computer Assisted Radiology and Surgery (2000) Multimodal Volume-based Tumor Neurosurgery Planning in the Virtual Workbench. Serra, L., Kockro, R.A., Guan, C.G., Hern, N., Lee, E.C.K., Lee, Y.H., Chan, C., Nowinski, W.L. In MICCAI (1998)

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