Multimodal Neurosurgery Planning

Contact: O. El Ganaoui, C. Barillot, P. Coupé, B. Gibaud, P. Hellier, X. Morandi, P. Paul, S. Prima, N. Wiest-Daesslé, P. Jannin

Context

The planning step for multimodal image guided neurosurgery consist in maximizing the relevant information available from different preoperative image modalities. The selected information extracted from each image, shall be available for surgical planning and needs to be presented to the neurosurgeon in a unified way. Therefore, pre-operative images from different and complementary modalities (3D-MRI, functional MRI,...) are geometrically registered on a selected reference image. The goal is to display different regions such as cortical sulci, lesional and functional areas.

Methods

For this example, the extracted information comes from different kind of imaging sequences such as:

• Magnetic Resonance Imaging (MRI)
• functional MRI (fMRI)
• diffusion MRI (Diffusion Weighted Imaging - DWI).

The extraction of different objects was performed thanks to different algorithms :

• From T1 weighted MRI [5,3,4]
  • Lesion area: red and green surface
  • Brain: light grey with low opacity
  • Sulci map: light yellow lines
  • Selected Sulci: yellow surface
• From functional MRI [1]
• Motricity area: blue balls
• Somesthesical area: orange balls
• From diffusion weighted MRI [2]
• fibers in the lesion area: light blue lines.

Références

[1] Jannin,P., Morandi,X., Fleig, O., Le Rumeur,E., Toulouse,P., Gibaud,B. and Scarabin, J-M. Integration of sulcal and functional information for multimodal neuronavigation. J.Neurosurgery, Vol.96, No4 pp 713-23, 2002.

[2] Mori, S. and Zijl, P.C. Fiber tracking: principles and strategies - a technical review. NMR in Biomedicine, 15(7-8):468-480, Nov-Dec 2002.

[3] C. Ciofolo, C. Barillot. Brain Segmentation with Competitive Level Sets and Fuzzy Control. In 19th International Conference on Information Processing in Medical Imaging, IPMI'2005, G.E. Christensen, M. Sonka (eds.), Lecture Notes in Computer Science, Volume 3565, Pages 333-344, Glenwood Springs, États-Unis, Juillet 2005.

[4] G. Le Goualher, C. Barillot, Y. Bizais.Three-dimensional segmentation and representation of cortical sulci using active ribbons. Int. J. of Pattern Recognition and Artificial Intelligence, 11(8):1295-1315, 1997.

[5] P. A. Yushkevich, J. Piven, H Cody, S. Ho, J.C. Gee and G. Gerig User-Guided Level Set Segmentation of Anatomical Structures with ITK-SNAP, Insight Journal, Special Issue on ISC/NA-MIC/MICCAI Workshop on Open-Source Software, Nov 2005. itk-SNAP