Tracking Cortical Surface Deformation Using Stereovision
Tracking brain deformation is important for the understanding of brain biomechanical properties. However, accurate deformation tracking may be challenging especially in vivo. We present a completely noninvasive technique to track cortical surface deformation using intraoperative stereovision during open cranial neurosurgery. A sequence of stereo images was acquired to capture motion of the exposed cortical surface due to blood pressure pulsation during multiple respiration cycles for patients undergoing brain tumor resection surgery. Rigid registration was performed between the first and subsequent frames using features outside the craniotomy to compensate for accidental motion of the surgical microscope. A nonrigid registration based on optical flow was performed next between the first and subsequent frames. A reference image was then generated using pixel displacements averaged across an integer multiple of respiration cycles to serve as a reference state based on which a dense displacement field was determined for each image frame. The resulting displacement field was then locally smoothed to minimize noise, and was further spatially differentiated to compute in-plane surface strain using deformation gradient. The technique offers an effective approach to track deformation of soft tissue surface as long as sufficient tracking features are available, which is useful for soft tissue biomechanical characterization.
KeywordsCortical surface deformation Cortical surface strain Stereovision Optical flow
This work was supported in part by National Institutes of Health grant number R01 CA159324–01 awarded by the National Cancer Institute.
- 1.Fan X, Ji S, Hartov A, Roberts D, Paulsen K (2012) Registering stereovision surface with preoperative magnetic resonance images for brain shift compensation In: Holmes DR III, Wong KH (eds) Medical imaging 2012: image-guided procedures, robotic interventions, and modeling. Proceedings of SPIE, vol 8316 (SPIE, Bellingham, WA 2012) 83161CGoogle Scholar
- 5.Ji S, Fan X, Roberts DW, Paulsen KD (2011) Cortical surface strain estimation using stereovision. In: Fichtinger G, Martel A, Peters T (eds) MICCAI 2011, Part I, LNCS 6891, pp 412–419Google Scholar
- 7.Lucas BD, Kanade T (1981) An Iterative Image Registration Technique with an Application to Stereo Vision (DARPA), Proceedings of the 1981 DARPA Image Understanding Workshop, April 1981, pp. 121–130Google Scholar
- 8.Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36Google Scholar
- 10.Meer P (2004) Robust techniques for computer vision. Emerging Topics in Computer Vision, Gerard Medioni and Sing Bing Kang (Eds.), Prentice Hall, 107–190Google Scholar
- 11.Liu C (2009) Beyond pixels: exploring new representations and applications for motion analysis. Doctoral thesis, Massachusetts Institute of Technology, May 2009Google Scholar
- 12.Brigham EO (2002) The fast Fourier Transform. Prentice-Hall, New YorkGoogle Scholar
- 15.Lai WM, Rubin D, Krempl E (1993) Introduction to continuum mechanics. Pergamon, OxfordGoogle Scholar