Visualization and Planning of Neurosurgical Interventions with Straight Access

Image-guided neurosurgical interventional procedures utilize medical imaging techniques to identify the most appropriate path for accessing a targeted structure. Often, preoperative planning entails the use of multi-contrast or multi-modal imaging for assessing different aspects of patient's pathophysiology related to the procedure. Comprehensive visualization and manipulation of such large volume of three-dimensional anatomical information is a major challenge. In this work we propose a technique for simple and efficient visualization of the region of intervention for neurosurgical procedures. It is done through the generation of access maps on the surface of the patient's skin, which assists a neurosurgeon in selecting the most appropriate path of access by avoiding vital structures and minimizing potential trauma to healthy tissue. Our preliminary evaluation showed that this technique is effective as well as easy to use for planning neurosurgical interventions such as biopsies, deep brain stimulation, ablation of brain lesions.

[1]  Nicholas Ayache,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I , 2007, MICCAI.

[2]  E. De Momi,et al.  An intelligent atlas-based planning system for keyhole neurosurgery , 2009 .

[3]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[4]  Suhuai Luo,et al.  Extraction of Brain Vessels from Magnetic Resonance Angiographic Images: Concise Literature Review, Challenges, and Proposals , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[5]  Nikolaos G. Bourbakis,et al.  A 3-D visualization method for image-guided brain surgery , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[6]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[7]  Dustin Scheinost,et al.  Novel interaction techniques for neurosurgical planning and stereotactic navigation , 2008, IEEE Transactions on Visualization and Computer Graphics.

[8]  Wieslaw Lucjan Nowinski,et al.  Virtual reality in brain intervention , 2004, Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering.

[9]  Luis Serra,et al.  Volume-based tumor neurosurgery planning in the Virtual Workbench , 1998, Proceedings. IEEE 1998 Virtual Reality Annual International Symposium (Cat. No.98CB36180).

[10]  Jean-Marie Scarabin,et al.  Multimodal and multi-informational neuronavigation , 2000 .

[11]  Hiroshi Emoto,et al.  Neuropath planner-automatic path searching for neurosurgery , 2003, CARS.

[12]  Jiann-Der Lee,et al.  Improving stereotactic surgery using 3-D reconstruction , 2002, IEEE Engineering in Medicine and Biology Magazine.

[13]  Bart M. ter Haar Romeny,et al.  Automatic Trajectory Planning for Deep Brain Stimulation: A Feasibility Study , 2007, MICCAI.

[14]  Markus Hadwiger,et al.  High-Quality Multimodal Volume Rendering for Preoperative Planning of Neurosurgical Interventions , 2007, IEEE Transactions on Visualization and Computer Graphics.

[15]  Heinrich Müller,et al.  Improved Laplacian Smoothing of Noisy Surface Meshes , 1999, Comput. Graph. Forum.

[16]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[17]  Russell H. Taylor,et al.  A path-planning algorithm for image-guided neurosurgery , 1997, CVRMed.