Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration

Stereovision is an important intraoperative imaging technique that captures the exposed parenchymal surface noninvasively during open cranial surgery. Estimating cortical surface shift efficiently and accurately is critical to compensate for brain deformation in the operating room (OR). In this study, we present an automatic and robust registration technique based on optical flow (OF) motion tracking to compensate for cortical surface displacement throughout surgery. Stereo images of the cortical surface were acquired at multiple time points after dural opening to reconstruct three-dimensional (3D) texture intensity-encoded cortical surfaces. A local coordinate system was established with its z-axis parallel to the average surface normal direction of the reconstructed cortical surface immediately after dural opening in order to produce two-dimensional (2D) projection images. A dense displacement field between the two projection images was determined directly from OF motion tracking without the need for feature identification or tracking. The starting and end points of the displacement vectors on the two cortical surfaces were then obtained following spatial mapping inversion to produce the full 3D displacement of the exposed cortical surface. We evaluated the technique with images obtained from digital phantoms and 18 surgical cases - 10 of which involved independent measurements of feature locations acquired with a tracked stylus for accuracy comparisons, and 8 others of which 4 involved stereo image acquisitions at three or more time points during surgery to illustrate utility throughout a procedure. Results from the digital phantom images were very accurate (0.05 pixels). In the 10 surgical cases with independently digitized point locations, the average agreement between feature coordinates derived from the cortical surface reconstructions was 1.7-2.1mm relative to those determined with the tracked stylus probe. The agreement in feature displacement tracking was also comparable to tracked probe data (difference in displacement magnitude was <1mm on average). The average magnitude of cortical surface displacement was 7.9 ± 5.7 mm (range 0.3-24.4 mm) in all patient cases with the displacement components along gravity being 5.2 ± 6.0 mm relative to the lateral movement of 2.4 ± 1.6 mm. Thus, our technique appears to be sufficiently accurate and computationally efficiency (typically ∼15 s), for applications in the OR.

[1]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[2]  James S. Duncan,et al.  Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery , 2010, IEEE Transactions on Medical Imaging.

[3]  T. Peters,et al.  Intraoperative ultrasound for guidance and tissue shift correction in image-guided neurosurgery. , 2000, Medical physics.

[4]  Benoit M. Dawant,et al.  Phantom-based comparison of the accuracy of point clouds extracted from stereo cameras and laser range scanner , 2013, Medical Imaging.

[5]  M. Y. Wang,et al.  Measurement of Intraoperative Brain Surface Deformation Under a Craniotomy , 1998, MICCAI.

[6]  Robert J. Maciunas,et al.  Registration of head volume images using implantable fiducial markers , 1997, IEEE Transactions on Medical Imaging.

[7]  Derek L. G. Hill,et al.  Measurement of Intraoperative Brain Surface Deformation Under a Craniotomy , 1998, MICCAI.

[8]  Keith D. Paulsen,et al.  Registering stereovision surface with preoperative magnetic resonance images for brain shift compensation , 2012, Medical Imaging.

[9]  Ce Liu,et al.  Exploring new representations and applications for motion analysis , 2009 .

[10]  Benoit M. Dawant,et al.  Semiautomatic Registration of Pre- and Postbrain Tumor Resection Laser Range Data: Method and Validation , 2009, IEEE Transactions on Biomedical Engineering.

[11]  Ron Kikinis,et al.  Serial registration of intraoperative MR images of the brain , 2002, Medical Image Anal..

[12]  Peter Meer,et al.  ROBUST TECHNIQUES FOR COMPUTER VISION , 2004 .

[13]  Robert L. Galloway,et al.  Cortical surface registration for image-guided neurosurgery using laser-range scanning , 2003, IEEE Transactions on Medical Imaging.

[14]  Keith D. Paulsen,et al.  Stereopsis-guided brain shift compensation , 2005, IEEE Transactions on Medical Imaging.

[15]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[16]  Pierre Jannin,et al.  A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery , 2009, IEEE Transactions on Information Technology in Biomedicine.

[17]  W. Hall,et al.  Safety, efficacy, and functionality of high-field strength interventional magnetic resonance imaging for neurosurgery. , 2000, Neurosurgery.

[18]  N. Hata,et al.  Serial Intraoperative Magnetic Resonance Imaging of Brain Shift , 2001, Neurosurgery.

[19]  James S. Duncan,et al.  Nonrigid 3D Brain Registration Using Intensity/Feature Information , 2006, MICCAI.

[20]  K. Paulsen,et al.  Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. , 1998 .

[21]  David J. Hawkes,et al.  Design and evaluation of a system for microscope-assisted guided interventions (MAGI) , 2000 .

[22]  Valerie Duay,et al.  A method to track cortical surface deformations using a laser range scanner , 2005, IEEE Transactions on Medical Imaging.

[23]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[24]  Benoit M. Dawant,et al.  An atlas-based method to compensate for brain shift: Preliminary results , 2007, Medical Image Anal..

[25]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[26]  R D Bucholz,et al.  Three-dimensional localization: from image-guided surgery to information-guided therapy. , 2001, Methods.

[27]  Keith D. Paulsen,et al.  Cortical Surface Strain Estimation Using Stereovision , 2011, MICCAI.

[28]  V. Tronnier,et al.  Intraoperative magnetic resonance imaging to update interactive navigation in neurosurgery: method and preliminary experience. , 1997, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[29]  Benoit M. Dawant,et al.  Tracking of Vessels in Intra-Operative Microscope Video Sequences for Cortical Displacement Estimation , 2011, IEEE Transactions on Biomedical Engineering.

[30]  Keith D. Paulsen,et al.  An integrated model-based neurosurgical guidance system , 2010, Medical Imaging.

[31]  Pierre Jannin,et al.  Augmented virtuality based on stereoscopic reconstruction in multimodal image-guided neurosurgery: methods and performance evaluation , 2005, IEEE Transactions on Medical Imaging.

[32]  Haiying Liu,et al.  Measurement and analysis of brain deformation during neurosurgery , 2003, IEEE Transactions on Medical Imaging.

[33]  V. Tronnier,et al.  Initial Experience with an Ultrasound-Integrated Single-Rack Neuronavigation System , 2001, Acta Neurochirurgica.

[34]  Wolfgang Birkfellner,et al.  A fully automated calibration method for an optical see-through head-mounted operating microscope with variable zoom and focus , 2005, IEEE Transactions on Medical Imaging.

[35]  Keith D. Paulsen,et al.  Efficient Stereo Image Geometrical Reconstruction at Arbitrary Camera Settings from a Single Calibration , 2014, MICCAI.

[36]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..