Deformable registration for image-guided spine surgery: preserving rigid body vertebral morphology in free-form transformations

Purpose: Deformable registration of preoperative and intraoperative images facilitates accurate localization of target and critical anatomy in image-guided spine surgery. However, conventional deformable registration fails to preserve the morphology of rigid bone anatomy and can impart distortions that confound high-precision intervention. We propose a constrained registration method that preserves rigid morphology while allowing deformation of surrounding soft tissues. Method: The registration method aligns preoperative 3D CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with penalties on rigid body motion imposed according to a simple intensity threshold. The penalties enforced 3 properties of a rigid transformation – namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments (involving phantoms, an ovine spine, and a human cadaver) as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (denoted uFFD) and Demons registration. Result: FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (D = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (S = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. Conclusions: A promising method for deformable registration in CBCT-guided spine surgery has been identified incorporating a constrained FFD to preserve bone morphology. The approach overcomes distortions intrinsic to unconstrained FFD and could better facilitate high-precision image-guided spine surgery.

[1]  Torsten Rohlfing,et al.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint , 2003, IEEE Transactions on Medical Imaging.

[2]  Simon K. Warfield,et al.  Estimation of the deformations induced by articulated bodies: Registration of the spinal column , 2007, Biomed. Signal Process. Control..

[3]  Fumihiko Ino,et al.  A data distributed parallel algorithm for nonrigid image registration , 2005, Parallel Comput..

[4]  Paul Suetens,et al.  Nonrigid Image Registration Using Free-Form Deformations with a Local Rigidity Constraint , 2004, MICCAI.

[5]  Farida Cheriet,et al.  3D registration of MR and X-ray spine images using an articulated model , 2012, Comput. Medical Imaging Graph..

[6]  Jeffrey A. Fessler,et al.  A Simple Regularizer for B-spline Nonrigid Image Registration That Encourages Local Invertibility , 2009, IEEE Journal of Selected Topics in Signal Processing.

[7]  Alpesh A. Patel,et al.  The Development and Evaluation of the Subaxial Injury Classification Scoring System for Cervical Spine Trauma , 2011, Clinical orthopaedics and related research.

[8]  Peer Eysel,et al.  The treatment of spinal metastases. , 2011, Deutsches Arzteblatt international.

[9]  Gary E. Christensen,et al.  Consistent image registration , 2001, IEEE Transactions on Medical Imaging.

[10]  I. Bloch,et al.  Combining a breathing model and tumor-specific rigidity constraints for registration of CT-PET thoracic data , 2008, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[11]  David R. Haynor,et al.  Multirigid registration of MR and CT images of the cervical spine , 2004, SPIE Medical Imaging.

[12]  Guozhi Tao,et al.  Symmetric inverse consistent nonlinear registration driven by mutual information , 2009, Comput. Methods Programs Biomed..

[13]  Yan Wang,et al.  Application of Intraoperative Computed Tomography With or Without Navigation System in Surgical Correction of Spinal Deformity: A Preliminary Result of 59 Consecutive Human Cases , 2012, Spine.

[14]  Nikolaos K. Paschos,et al.  Accuracy of pedicle screw placement: a systematic review of prospective in vivo studies comparing free hand, fluoroscopy guidance and navigation techniques , 2012, European Spine Journal.

[15]  F. Girardi,et al.  Degenerative Scoliosis: A Review , 2011, HSS Journal ®.

[16]  M. Staring,et al.  A rigidity penalty term for nonrigid registration. , 2007, Medical physics.

[17]  Daniel Rueckert,et al.  Diffeomorphic Registration Using B-Splines , 2006, MICCAI.

[18]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[19]  Nicholas Ayache,et al.  A Log-Euclidean Framework for Statistics on Diffeomorphisms , 2006, MICCAI.

[20]  D. Hill,et al.  Deformations incorporating rigid structures [medical imaging] , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[21]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[22]  Seungyong Lee,et al.  Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions , 2000, Graph. Model..

[23]  Olaf Suess,et al.  The Sforzesco brace can replace cast in the correction of adolescent idiopathic scoliosis: A controlled prospective cohort study , 2008, Scoliosis.

[24]  Cristian Lorenz,et al.  Spine Segmentation Using Articulated Shape Models , 2008, MICCAI.

[25]  B. Wilson,et al.  Volume CT with a flat-panel detector on a mobile, isocentric C-arm: pre-clinical investigation in guidance of minimally invasive surgery. , 2005, Medical physics.

[26]  Xia Liu,et al.  Topology preservation evaluation of compact-support radial basis functions for image registration , 2011, Pattern Recognit. Lett..

[27]  Nicholas Ayache,et al.  c ○ 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. Isotropic Energies, Filters and Splines for Vector Field Regularization , 2022 .

[28]  Aly A. Farag,et al.  A Novel 3D Segmentation of Vertebral Bones from Volumetric CT Images Using Graph Cuts , 2009, ISVC.

[29]  Wei Lu,et al.  Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry. , 2007, Medical physics.

[30]  Michael G Fehlings,et al.  Timing of surgical intervention in spinal trauma: what does the evidence indicate? , 2010, Spine.

[31]  Jan Modersitzki,et al.  FLIRT with Rigidity—Image Registration with a Local Non-rigidity Penalty , 2008, International Journal of Computer Vision.

[32]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[33]  Stephen L. Keeling,et al.  Medical Image Registration and Interpolation by Optical Flow with Maximal Rigidity , 2005, Journal of Mathematical Imaging and Vision.

[34]  K. Bridwell,et al.  Risk-Benefit Assessment of Surgery for Adult Scoliosis: An Analysis Based on Patient Age , 2011, Spine.

[35]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[36]  A Uneri,et al.  Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery , 2013, Physics in medicine and biology.