Non-rigid Deformation Pipeline for Compensation of Superficial Brain Shift
The correct visualization of anatomical structures is a critical component of neurosurgical navigation systems, to guide the surgeon to the areas of interest as well as to avoid brain damage. A major challenge for neuronavigation systems is the brain shift, or deformation of the exposed brain in comparison to preoperative Magnetic Resonance (MR) image sets. In this work paper, a non-rigid deformation pipeline is proposed for brain shift compensation of preoperative imaging datasets using superficial blood vessels as landmarks. The input was preoperative and intraoperative 3D image sets of superficial vessel centerlines. The intraoperative vessels (obtained using 3 Near-Infrared cameras) were registered and aligned with preoperative Magnetic Resonance Angiography vessel centerlines using manual interaction for the rigid transformation and, for the non-rigid transformation, the non-rigid point set registration method Coherent Point Drift. The rigid registration transforms the intraoperative points from the camera coordinate system to the preoperative MR coordinate system, and the non-rigid registration deals with local transformations in the MR coordinate system. Finally, the generation of a new deformed volume is achieved with the Thin-Plate Spline (TPS) method using as control points the matches in the MR coordinate system found in the previous step. The method was tested in a rabbit brain exposed via craniotomy, where deformations were produced by a balloon inserted into the brain. There was a good correlation between the real state of the brain and the deformed volume obtained using the pipeline. Maximum displacements were approximately 4.0 mm for the exposed brain alone, and 6.7 mm after balloon inflation.
KeywordsRigid Registration Rabbit Brain Brain Shift Preoperative Magnetic Resonance Neuronavigation System
- 1.Marreiros, F., Rossitti, S., Gustafsson, T., Carleberg, P., Smedby, Ö.: Multi-view 3D vessel tracking using near-infrared cameras. In: Proc. of CARS 2013: Image Processing and Visualization (2013)Google Scholar
- 2.D’Apuzzo, D., Verius, M.: 3D Monitoring of the Intraoperative Brainshift by Means of Photogrammetry. In: Proc. of SPIE-IS&T Electronic Imaging, vol. 6805 (2008)Google Scholar
- 3.Hsieh, C., Huang, C., Lee, J., Lee, S., Wu, C.: Estimation of Brain-Surface Deformation with Computer Vision. International Journal of Intelligent Information Processing (IJIIP) 3(1) (2012)Google Scholar
- 5.Chui, H., Rangarajan, A.: A New Algorithm for Non-Rigid Point Matching. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2262–2275 (2000)Google Scholar
- 13.Wang, C., Frimmel, H., Smedby, Ö.: Level-set based vessel segmentation accelerated with periodic monotonic speed function. In: Proc. SPIE 7962, Medical Imaging 2011: Image Processing. Society of Photo-Optical Instrumentation Engineers (2011)Google Scholar