Abstract
Accurate registration of patient anatomy, obtained from intra-operative ultrasound (US) and preoperative computed tomography (CT) images, is an essential step to a successful US-guided computer assisted orthopaedic surgery (CAOS). Most state-of-the-art registration methods in CAOS require either significant manual interaction from the user or are not robust to the typical US artifacts. Furthermore, one of the major stumbling blocks facing existing methods is the requirement of an optimization procedure during the registration, which is time consuming and generally breaks when the initial misalignment between the two registering data sets is large. Finally, due to the limited field of view of US imaging, obtaining scans of the full anatomy is problematic, which causes difficulties during registration. In this paper, we present a new method that registers local phase-based bone features in frequency domain using image projections calculated from three-dimensional (3D) radon transform. The method is fully automatic, non-iterative, and requires no initial alignment between the two registering datasets. We also show the method’s capability in registering partial view US data to full view CT data. Experiments, carried out on a phantom and six clinical pelvis scans, show an average 0.8 mm root-mean-square registration error.
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Nolte, L.P., Zamorano, L.J., Jiang, Z., Wang, Q., Langlotz, F., Berlemann, F.: Image-guided insertion of transpedicular screws. A Laboratory Set-Up. Spine 20(4), 497–500 (1995)
Jaramaz, B., DiGioia, A.M., Blackwell, M., Nikou, C.: Computer assisted measurement of cup placement in total hip replacement. Clinical Orthopaedics 354, 70–81 (1998)
Tyryshkin, K., Mousavi, P., Beek, M., Ellis, R., Dichora, P., Abolmaesumi, P.: A navigation system for shoulder arthroscopic surgery. Journal of Engineering in Medicine: Special Issue on Navigation Systems in Computer-assisted Orthopaedic Surgery, 801–812 (2007)
Hacihaliloglu, I., Abugharbieh, R., Hodgson, A., Rohling, R.: Bone Surface Localization in Ultrasound Using Image Phase Based Features. Ultrasound in Med. and Biol. 35(9), 1475–1487 (2009)
Penney, G.P., Edwards, P.J., King, A.P., Blackall, J.M., Batchelor, P.G., Hawkes, D.J.: A Stochastic Iterative Closest Point Algorithm (stochastICP). In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 762–769. Springer, Heidelberg (2001)
Moghari, M.H., Abolmaesumi, P.: Point-Based Rigid-Body Registration Using an Unscented Kalman Filter. IEEE Transactions on Medical Imaging 26(12), 1708–1728 (2007)
Brounstein, A., Hacihaliloglu, I., Guy, P., Hodgson, A., Abugharbieh, R.: Towards Real-Time 3D US to CT Bone Image Registration Using Phase and Curvature Feature Based GMM Matching. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 235–242. Springer, Heidelberg (2011)
Brendel, B., Winter, S., Rick, A., Stockheim, M., Ermert, H.: Registration of 3D CT and Ultrasound Datasets of the Spine Using Bone Structures. Computer Aided Surgery 7, 146–155 (2002)
Penney, G., Barratt, D., Chan, C., Slomczykowski, M., Carter, T., Edwards, P., Hawkes, D.: Cadaver Validation of Intensity-Based Ultrasound to CT Registration. Medical Image Analysis 10(3), 385–395 (2006)
Gill, S., Abolmaesumi, P., Fichtinger, G., Boisvert, J., Pichora, D., Borshneck, D., Mousavi, P.: Biomechanically Constrained Groupwise Ultrasound to CT Registration of the Lumbar Spine. Medical Image Analysis (2010) (in Press)
Khallaghi, S., Mousavi, P., Borschneck, D., Fichtinger, G., Abolmaesumi, P.: Biomechanically Constrained Groupwise Statistical Shape Model to Ultrasound Registration of the Lumbar Spine. In: Taylor, R.H., Yang, G.-Z. (eds.) IPCAI 2011. LNCS, vol. 6689, pp. 47–54. Springer, Heidelberg (2011)
Foroughi, P., Boctor, E., Swatrz, M.J., Taylor, R.H., Fichtinger, G.: Ultrasound bone segmentation using dynamic programming. In: IEEE Ultrasonics Syposium, pp. 2523–2526 (2007)
Hacihaliloglu, I., Abugharbieh, R., Hodgson, A.J., Rohling, R.: Automatic Bone Localization and Fracture Detection from Volumetric Ultrasound Images Using 3D Local Phase Features. Ultrasound in Medicine and Biology 38(1), 128–144 (2011)
Tsuboi, T., Hirai, S.: Detection of Planar Motion Objects Using Radon Transform and One-Dimensional Phase-Only Matched Filtering. Systems and Computers in Japan 37(5), 1963–1972 (2006)
Gurbuz, A.C., McClellan, J.H., Romberg, J., Scott, W.R.: Compressive sensing of parameterized shapes in images. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1949–1952 (2008)
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Hacihaliloglu, I., Wilson, D.R., Gilbart, M., Hunt, M., Abolmaesumi, P. (2012). Non-iterative Multi-modal Partial View to Full View Image Registration Using Local Phase-Based Image Projections. In: Abolmaesumi, P., Joskowicz, L., Navab, N., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2012. Lecture Notes in Computer Science, vol 7330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30618-1_7
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DOI: https://doi.org/10.1007/978-3-642-30618-1_7
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