Document Type : Full Research Paper

Authors

1 M.Sc Graduated, physic and Biomedical Engineering, Tehran University of Medical Sciences

2 Associate Professor, physic and Biomedical Engineering, Tehran University of Medical Sciences

3 Associate Professor,Surgery Grup of brain and Nerves, Tehran University of Medical Sciences

10.22041/ijbme.2013.13089

Abstract

Registration of preoperative images to intra-operative patient space is a crucial step in image guided surgery for tracking surgical tools relative to patient’s anatomy. In image guided spine surgery, due to the difference in patient’s positioning in preoperative imaging, compared with intra-operative situation, there is a difference in spine curvature in these two positioning which means that a single rigid registration is not sufficient for registering the whole spine and it is necessary for each vertebra to be registered separately as a rigid body and with it’s appropriate transformation parameters. The registration was carried out using ICP algorithm. For evaluating the registration, TRE was calculated in the pedicle of the vertebra which is the target in pedicle screw insertion. In order to optimize the TRE this study was focused on the factors affecting TRE including different configuration of landmarks used in registration and the registration algorithm. Optimal configurations for the landmarks used in the registration were proposed and FLE for the point pairs were included in the registration algorithm to increase the registration accuracy. The results indicate a total improvement of 45% in the registration accuracy by optimizing the landmarks’ configuration and the registration algorithm.

Keywords

Main Subjects

[1]     Nolte LP, Slomczykowski MA, Berlemann U, Strauss MJ, Hofstetter R, Schlenzka D, Laine T, Lund T (2000) A new approach to computer-aided spine surgery: fluoroscopy-based surgical navigation. European Spine Journal 9 (7):S078-S088
[2]     Arun KS, Huang TS, Blostein SD (1987) Least-Squares Fitting of Two 3-D Point Sets. Pattern Analysis and Machine Intelligence, IEEE Transactions on PAMI-9 (5):698-700. doi:10.1109/tpami.1987.4767965
[3]     Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4 (4):629-642
[4]     Balachandran R, Fitzpatrick aJM (2009) Iterative Solution for Rigid-Body Point-Based Registration with  Anisotropic Weighting.
[5]     Fitzpatrick JM, West JB (2001) The distribution of target registration error in rigid-body point-based registration. IEEE Trans Med Imaging 20 (9):917-927. doi:10.1109/42.952729 [doi]
[6]     West JB, Fitzpatrick JM, Toms SA, Maurer CR, Jr., Maciunas RJ (2001) Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery 48 (4):810-816; discussion 816-817
[7]     Danilchenko A, Fitzpatrick JM (2011) General Approach to First-Order Error Prediction in Rigid Point Registration. Medical Imaging, IEEE Transactions on 30 (3):679-693. doi:10.1109/tmi.2010.2091513
[8]     Ma B, Moghari M, Ellis R, Abolmaesumi P (2007) On Fiducial Target Registration Error in the Presence of Anisotropic Noise. In: Ayache N, Ourselin S, Maeder A (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, vol 4792. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp 628-635. doi:10.1007/978-3-540-75759-7_76