Minimization of target registration error for vertebra in image-guided spine surgery
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The accuracy of pedicle screw placement during image-guided spine surgery (IGSS) can be characterized by estimating the target registration error (TRE). The major factors that influence TRE were identified, minimized, and verified with in vitro experiments.
Materials and methods
Computed-tomography-compatible markers are placed over anatomical landmarks of lumbar vertebral segments in locations that are feasible and routinely used in surgical procedures. TRE was determined directly for markers placed on the pedicles of vertebra segments. First, optimum selections of landmarks are proposed for different landmarks according to the minimum achievable TRE values in different configurations. These anatomical landmarks are feasible and accessible to overcome constraints that may be imposed during surgical procedures. Second, the effect of fiducial weighting on corresponding points to overcome anisotropic localization error based on maximum likelihood approach is evaluated. Third, an experimental model for fiducial localization error (FLE) is derived to obtain the weights. At the end, an error zone was obtained for each marker to indicate the possible acceptable deviation from the marker’s exact location in practice. This study was performed in vitro on a spine phantom.
Optimal landmark selection led to a 30 % reduction in TRE. In addition, optimum weighting of the fiducials in an FLE model that incorporates anisotropic localization error in the registration algorithm led to a 28 % reduction in the TRE.
Landmark configuration, transformation parameters, and fiducial localization error are factors that significantly affect the total TRE. These factors should be optimized to minimize the TRE. Both the optimum configuration of landmarks and the anisotropic weighing of fiducials have significant impact on the registration accuracy for IGSS.
KeywordsTarget registration error Fiducial localization error Rigid registration Image-guided surgery
The authors would like to thank the reviewers for carefully reading the paper and providing valuable comments and suggestions. The first author would also like to thank Professor J. Michael Fitzpatrick for helpful communication.
Conflict of Interest
Marzieh Ershad, Alireza Ahmadian, Nassim Dadashi Serej, Hooshang Saberi, and Keyvan Amini Khoiy declare that they have no conflict of interest
- 2.West JB et al (2001) Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery 48:810–816; discussion 816–817Google Scholar
- 3.Shamir RR et al (2009) Optimal landmarks selection and fiducial marker placement for minimal target registration error in image-guided neurosurgery. In: SPIE medical imaging visualization, image-guided procedures, and modelingGoogle Scholar
- 4.Shamir R et al (2009) Localization and registration accuracy in image guided neurosurgery: a clinical study. Int J Comput Assist Radiol Surg 4:45–52Google Scholar
- 6.Wang M, Song Z (2010) Guidelines for the placement of fiducial points in image-guided neurosurgery. Int J Med Robot 6: 142–149Google Scholar
- 8.Wang M, Song Z (2010) Distribution templates of the fiducial points in image-guided neurosurgery. Neurosurgery 66:143–150; discussion 150–151Google Scholar
- 9.Zhang W et al (2011) Effect of fiducial configuration on target registration error in image-guided cranio-maxillofacial surgery. J Cranio-Maxillofac Surg 39:407–411Google Scholar
- 10.Fitzpatrick JM (2009) Fiducial registration error and target registration error are uncorrelated. In: SPIE 7261, medical imaging 2009: visualization, image-guided procedures, and modeling, 726102 (March 13, 2009). doi: 10.1117/12.813601
- 18.Balachandran R, Fitzpatrick JM (2009) Iterative solution for rigid-body point-based registration with anisotropic weighting. In: Miga MI, Wong KH (eds) Proceedings of SPIE 7261, medical imaging 2009: visualization, image-guided procedures, and modeling, 72613D, 13 March 2009. doi: 10.1117/12.813887
- 19.Ohta N, Kanatani K (1998) Optimal estimation of three-dimensional rotation and reliability evaluation. In: Burkhardt H, Neumann B (eds) Computer vision–ECCV’98, vol 1406. Springer, Berlin, pp 175–187Google Scholar
- 20.West JB et al (2001) Point-based registration under a similarity transform. In: Proceedings of SPIE 4322, Medical Imaging 2001: Image Processing, 611, 3 July 2001. San Diego pp 611–622. doi: 10.1117/12.431135
- 21.Maier-Hein L et al (2012) Convergent iterative closest-point algorithm to accommodate anisotropic and inhomogenous localization error. IEEE Trans Pattern Anal Mach Intell 34:1520–1532Google Scholar
- 22.Shamir RR et al (2009) Localization and registration accuracy in image guided neurosurgery: a clinical study. Int J Comput Assist Radiol Surg 4:45–52Google Scholar
- 23.Danilchenko A, Fitzpatrick JM (2011) General approach to first-order error prediction in rigid point registration. IEEE Trans Med Imaging 30:679–693 Google Scholar
- 24.Maier-Hein L et al (2011) Iterative closest point algorithm with anisotropic weighting and its application to fine surface registration. In: Proceedings of the SPIE 7962, medical imaging 2011: image processing, 79620W (March 11, 2011). doi: 10.1117/12.873060