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Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective

We present a novel method for intraoperative image-based bone surface reconstruction and its validation.

Materials and methods

In the preoperative stage, we construct a CT-like intensity atlas of the anatomy of interest. In the intraoperative stage, we deformably register this atlas to fluoroscopic X-ray images of the patient anatomy. We iteratively refine the atlas-to-patient registration by establishing explicit correspondences between bone surfaces in the atlas and their projections in the X-ray images. The advantage of our method is its use of CT-quality intensity data for correspondence establishment, which eliminates the edge-detection problem and diminishes the miscorrespondence problem. We validate our method on two datasets: (1) an in vitro dry femur; (2) Digitally Reconstructed Radiographs, which were generated from 17 clinical CTs, and simulate realistic in vivo femurs.

Results

The mean surface approximation error of our femur atlas was 0.85 ± 0.16 mm. On Digitally Reconstructed Radiographs, the mean surface reconstruction error was 1.40 ± 0.55 mm. On a dry femur, the mean surface reconstruction error was 1.44 mm.

Conclusion

The results show that our reconstruction method is on par with the state of the art in reconstruction of ex vivo femurs. In addition, the results demonstrate that our method is effective in realistic simulations of the in vivo scenario.

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References

  1. Maintz J, Viergever M (1998) A survey of medical image registration. Med Image Anal 2(1): 1–37

    Article  PubMed  CAS  Google Scholar 

  2. Joskowicz L, Knaan D, Livyatan H, Yaniv Z, Khoury A, Mosheiff R, Liebergall M (2003) Anatomical image-based rigid registration between fluoroscopic X-ray and CT: methods comparison and experimental results. In: CARS, pp 419–425

  3. Bansal R, Staib L, Chen Z, Rangarajan A, Knisely J, Nath R, Duncan J (2003) Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation. IEEE Trans Med Imag 22: 29–49

    Article  Google Scholar 

  4. Sarrut D, Clippe S (2001) Geometrical transformation approximation for 2D/3D intensity-based registration of portal images and CT scan. In: MICCAI, pp 532–540

  5. Bifulco P, Cesarelli M, Allen R, Sansone M, Bracale M (2001) Automatic recognition of vertebral landmarks in fluoroscopic sequences for analysis of intervertebral kinematics. Med Biol Eng Comput 39(1): 65–75

    Article  PubMed  CAS  Google Scholar 

  6. Benameur S, Mignotte M, Parent S, Labelle H, Skalli W, Guise JD (2003) 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imag Graph 27(5): 321–337

    Article  Google Scholar 

  7. McLaughlin RA, Hipwell J, Hawkes DJ, Noble J, Byrne JV, Cox T (2002) A comparison of 2D–3D intensity-based registration and feature-based registration for neurointerventions. In: MICCAI, vol 2489/2002. Springer, Berlin, Heidelberg, pp 517–524

  8. Tang TSY, Ellis RE (2005) 2D/3D deformable registration using a hybrid atlas. In: MICCAI (2), pp 223–230

  9. Lamecker H, Wenckebach T, Hege HC (2006) Atlas-based 3D-shape reconstruction from X-ray images. Int Conf Pattern Recog (ICPR’) 2006(1): 371–374

    Article  Google Scholar 

  10. Benameur S, Mignotte M, Labelle H, Guise JD (2005) A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine. IEEE Trans Biomed Eng 52(12): 2041–2057

    Article  PubMed  Google Scholar 

  11. Yao J, Taylor R (2003) Assessing accuracy factors in deformable 2D/3D medical image registration using a statistical pelvis model. In: Proc IEEE int conf comput vis (ICCV’03). IEEE Computer Society, Washington, DC, p 1329

  12. Chintalapani G, Ellingsen LM, Sadowsky O, Prince JL, Taylor RH (2007) Statistical atlases of bone anatomy: construction, iterative improvement and validation. In: MICCAI (1), pp 499–506

  13. Fleute M, Lavallée S (1999) Nonrigid 3D/2D registration of images using statistical models. In: MICCAI, pp 138–147

  14. Zheng G, Dong X, Ballester MÁ G (2007) Unsupervised reconstruction of a patient-specific surface model of a proximal femur from calibrated fluoroscopic images. In: MICCAI (1), pp 834–841

  15. Cootes TF, Edwards GJ, Taylor CJ (2003) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6): 681–685

    Article  Google Scholar 

  16. Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis (IJCV) 60(2): 135–164

    Article  Google Scholar 

  17. Ibanez L, Schroeder W, Ng L, Cates J (2005) The ITK software guide, 2nd edn. Kitware, Inc. ISBN 1-930934-15-7. http://www.itk.org/ItkSoftwareGuide.pdf

  18. Klein S, Staring M (2007) Elastix. Image Sciences Institute, University Medical Center Utrecht. http://www.isi.uu.nl/Elastix/

  19. Rueckert D, Frangi AF, Schnabel JA (2001) Automatic construction of 3D statistical deformation models using non-rigid registration. In: MICCAI, London, UK. Springer, Heidelberg, pp 77–84

  20. Si H (2006) TetGen: a quality tetrahedral mesh generator and three-dimensional delaunay triangulator. Weierstrass Institute for Applied Analysis and Stochastics, Berlin. http://tetgen.berlios.de/

  21. Livyatan H, Yaniv Z, Joskowicz L (2002) Robust automatic C-arm calibration for fluoroscopic-based navigation: a practical approach. In: MICCAI, vol II, pp 60–68

  22. Lemieux L, Jagoe R, Fish DR et al (1994) A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. Med Phys 21(11): 1749–1760

    Article  PubMed  CAS  Google Scholar 

  23. Penney G, Batchelor P, Hill D, Hawkes D, Weese J (2001) Validation of a two-to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images. Med Phys 28(6): 1024–1032

    Article  PubMed  CAS  Google Scholar 

  24. Staring M, Klein S, Pluim J (2007) A rigidity penalty term for nonrigid registration. Med Phys 34(11): 4098–4108

    Article  PubMed  Google Scholar 

  25. Lewis RM, Torczon V (1999) Pattern search algorithms for bound constrained minimization. SIAM J Opt 9(4): 1082–1099

    Article  Google Scholar 

  26. Marmitt G, Slusallek P (2006) Fast ray traversal of tetrahedral and hexahedral meshes for direct volume rendering. In: Ertl T, Joy KJ, Santos B (eds) Proc Eurographics/IEEE-VGTC Symp Visualization (EuroVIS’06), Lisbon, Portugal

  27. Aspert N, Santa-Cruz D, Ebrahimi T (2002) Mesh: measuring errors between surfaces using the hausdorff distance. In: Proc IEEE int conf on multimedia and expo (ICME), vol I, pp 705–708. http://mesh.epfl.ch

  28. Livyatan H, Yaniv Z, Joskowicz L (2003) Gradient-based 2D/3D rigid registration of fluoroscopic X-ray to CT. IEEE Trans Med Imag 22(11): 1395–1406

    Article  Google Scholar 

  29. Fleute M, Lavallée S, Desbat L (2002) Integrated approach for matching statistical shape models with intra-operative 2D and 3D data. In: MICCAI (2), pp 364–372

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Authors and Affiliations

  1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904, Jerusalem, Israel

    Aviv Hurvitz & Leo Joskowicz

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  1. Aviv Hurvitz
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  2. Leo Joskowicz
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Correspondence to Aviv Hurvitz.

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Hurvitz, A., Joskowicz, L. Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences. Int J CARS 3, 493–504 (2008). https://doi.org/10.1007/s11548-008-0264-z

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  • DOI: https://doi.org/10.1007/s11548-008-0264-z

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