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3D ultrasound-CT registration of the liver using combined landmark-intensity information

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

Abstract

Purpose

An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy.

Methods

One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration.

Results

A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks.

Conclusion

The proposed algorithm offers the possibility to incorporate additional a priori knowledge—in terms of few landmarks—provided by a human expert into a non-rigid registration process.

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References

  1. Bakalakos E, Kim J, Young D et al (1998) Determinants of survival following hepatic resection for metastatic colorectal cancer. World J Surg 22: 399–404

    Article  PubMed  CAS  Google Scholar 

  2. Fong Y, Fortner J, Sun R et al (1999) Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann Surg 230: 309–318

    Article  PubMed  CAS  Google Scholar 

  3. Abdalla E, Barnett C, Doherty D et al (2002) Extended hepatectomy in patients with hepatobiliary malignancies with and without preoperative portal vein embolization. Arch Surg 137: 675–680

    Article  PubMed  Google Scholar 

  4. Shirabe K, Shimada M, Gion T et al (1999) Postoperative liver failure after major hepatic resection for hepatocellular carcinoma in the modern era with special reference to remnant liver volume. J Am Coll Surg 188: 304–309

    Article  PubMed  CAS  Google Scholar 

  5. Shoup M, Gonen M, D’Angelica M et al (2003) Volumetric analysis predicts hepatic dysfunction in patients undergoing major liver resection. J Gastrointest Surg 7: 325–330

    Article  PubMed  Google Scholar 

  6. Vauthey J, Chaoui A, Do K et al (2000) Standardized measurement of the future liver remnant prior to extended liver resection: methodology and clinical associations. Surgery 127: 512–519

    Article  PubMed  CAS  Google Scholar 

  7. Redaelli C, Wagner M, Krahenbuhl L et al (2002) Liver surgery in the era of tissue-preserving resections: early and late outcome in patients with primary and secondary hepatic tumors. World J Surg 26: 1126–1132

    Article  PubMed  Google Scholar 

  8. Selle D, Preim B, Schenk A, Peitgen HO (2002) Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21(11): 1344–1357

    Article  PubMed  Google Scholar 

  9. Lang H, Radtke A, Hindennach M, Schroeder T, Fruhauf NR, Malago M et al (2005) Impact of virtual tumor resection and computer-assisted risk analysis on operation planning and intraoperative strategy in major hepatic resection. Arch Surg 140(7): 629–638

    Article  PubMed  Google Scholar 

  10. Cash DM, Miga MI, Glasgow SC, Dawant BM, Clements LW, Cao Z et al (2007) Concepts and Preliminary Data Toward the Realization of Image-guided Liver Surgery. J Gastrointest Surg 11(7): 844–859

    Article  PubMed  Google Scholar 

  11. Birth M, Kleemann M, Hildebrand P, Bruch HP (2004) Intraoperative online navigation of dissection of the hepatical tissue—a new dimension in liver surgery. In: CARS, pp 770–774

  12. Beller S, Hünerbein M, Eulenstein S, Lange T, Schlag P (2007) Feasibility of navigated resection of liver tumors using multiplanar visualization of intraoperative 3D ultrasound data. Ann Surg 246(2): 288–294

    Article  PubMed  Google Scholar 

  13. Beller S, Hünerbein M, Lange T, Eulenstein S, Gebauer B, Schlag PM (2007) Image-guided surgery of liver metastases by 3D ultrasound-based optoelectronic navigation. Brit J Surg 94(7): 866–875

    Article  PubMed  CAS  Google Scholar 

  14. Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans Med Imaging 20(10): 1038–1049

    Article  PubMed  CAS  Google Scholar 

  15. Slomka PJ, Mandel J, Downey D, Fenster A (2001) Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries. Ultrasound Med Biol 27(7): 945–955

    Article  PubMed  CAS  Google Scholar 

  16. Porter BC, Rubens DJ, Strang JG, Smith J, Totterman S, Parker KJ (2001) Three-dimensional registration and fusion of ultrasound and MRI using major vessels as fiducial markers. IEEE Trans Med Imaging 20(4): 354–359

    Article  PubMed  CAS  Google Scholar 

  17. Penney GP, Blackall JM, Hamady MS, Sabharwal T, Adam A, Hawkes DJ (2004) Registration of freehand 3D ultrasound and magnetic resonance liver images. Med Image Anal 8(1): 81–91

    Article  PubMed  CAS  Google Scholar 

  18. Lange T, Eulenstein S, Hünerbein M, Schlag PM (2003) Vessel-based non-rigid registration of MR/CT and 3D ultrasound for navigation in liver surgery. Comput Aided Surg 8(5): 228–240

    Article  PubMed  Google Scholar 

  19. Lange T, Eulenstein S, Hünerbein M, Lamecker H, Schlag P (2004) Augmenting intraoperative 3D ultrasound with preoperative models for navigation in liver surgery. In: Barillot C, Haynor D, Hellier P (eds) Medical image computing and computer-assisted intervention. Lecture notes in computer science, vol 3217. Springer, Berlin, pp 534–541

    Google Scholar 

  20. Reinertsen I, Lindseth F, Unsgaard G, Collins D (2007) Clinical validation of vessel-based registration for correction of brain-shift. Med Image Anal (in press)

  21. Reinertsen I, Descoteaux M, Siddiqi K, Collins D (2007) Validation of vessel-based registration for correction of brain shift. Med Image Anal 11(4): 374–388

    Article  PubMed  CAS  Google Scholar 

  22. Aylward SR, Jomier J, Weeks S, Bullitt E (2003) Registration and analysis of vascular images. Int J Comput Vision 55(2–3): 123–138

    Article  Google Scholar 

  23. Lange T, Lamecker H, Hünerbein M, Eulenstein S, Beller S, Schlag P et al (2007) A distance measure for non-rigid registration of geometrical models to intensity data. In: Lemke H et al (eds) CARS, vol 2 (Supp 1) of International Journal of Computer Assisted Radiology and Surgery. Springer, Berlin, pp 204–206

    Google Scholar 

  24. Haber E, Modersitzki J (2004) Numerical methods for volume preserving image registration. Inverse Probl 20: 1621–1638

    Article  Google Scholar 

  25. Modersitzki J (2007) Image registration with local rigidity constraints. In: Bildverarbeitung für die Medizin, pp 444–448

  26. Gobbi D, Comeau R, Peters T (2000) Ultrasound/mri overlay with image warping for neurosurgery. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, Berlin, pp 106–114

  27. Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intell 11(6): 567–585

    Article  Google Scholar 

  28. Rohr K (2001) Landmark-based image analysis. Springer, Berlin

    Google Scholar 

  29. Modersitzki J (2004) Numerical methods for image registration. Oxford University Press, Oxford

    Google Scholar 

  30. Davis MH, Khotanzad A, Flaming DP, Harms SE (1997) A physics-based coordinate transformation for 3-D image matching. IEEE Trans Med Imaging 16(3): 317–328

    Article  PubMed  CAS  Google Scholar 

  31. Kohlrausch J, Rohr K, Stiehl HS (2005) A new class of elastic body splines for nonrigid registration of medical images. J Math Imaging Vis 23: 253–280

    Article  Google Scholar 

  32. Rohr K, Stiehl H, Sprengel R, Buzug T, Weese J, Kuhn M (2001) Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans Med Imag 20(6): 526–534

    Article  CAS  Google Scholar 

  33. Wörz S, Rohr K (2006) Physics-based elastic image registration using splines and including landmark localization uncertainties. In: MICCAI (2), pp 678–685

  34. Papenberg N, Lange T, Modersitzki J, Schlag PM, Fischer B (2008) Image registration for CT and intra-operative ultrasound data of the liver. In: SPIE Medical imaging: visualization, image-guided procedures, and modeling, vol 6918 (accepted)

  35. Fischer B, Modersitzki J (2003) Combining landmarks and intensity driven registrations. In: PAMM Proceedings in Applied Mathematics and Mechanics. vol 3, pp 32–35

  36. Fischer B, Modersitzki J (2003) Combination of automatic non-rigid and landmark based registration: the best of both worlds. In: Sonka M, Fitzpatrick J (eds) Medical imaging 2003: image processing. Proceedings of the SPIE 5032, vol 5032, pp 1037–1048

  37. Haber E, Modersitzki J (2007) Intensity gradient based registration and fusion of multi-modal images. Methods Inf Med 46(3): 292–299

    PubMed  Google Scholar 

  38. Fischer B, Modersitzki J (2003) FLIRT: a flexible image registration toolbox. In: Gee J, Maintz J, Vannier M (eds) 2nd International Workshop on Biomedical Image Registration 2003, vol 2717. Springer, Berlin, pp 261–270

    Google Scholar 

  39. Papenberg N, Schumacher H, Heldmann S, Wirtz S, Bommersheim S, Ens K et al (2007) A fast and flexible image registration toolbox—design and implementation of the general approach. Bildverarbeitung für die Medizin 2007. Informatik Aktuell, pp 106–110

  40. Broit C (1981) Optimal registration of deformed images. Department of Computer and Information Science, University of Pennsylvania

  41. Haber E, Modersitzki J (2006) A multilevel method for image registration. SIAM J Sci Comput 27(5): 1594–1607

    Article  Google Scholar 

  42. Modersitzki J (2008) FLIRT with rigidity—image registration with a local non-rigidity penalty. Int J Comput Vis 76(2): 153–163

    Article  Google Scholar 

  43. Wahba G (1990) Spline models for observational data. SIAM, Philadelphia

  44. Nocedal J, Wright SJ (1999) Numerical optimization. Springer, Berlin

    Google Scholar 

  45. Lange T, Wenckebach T, Lamecker H, Seebass M, Hünerbein M, Eulenstein S et al (2005) Registration of different phases of constrast-enhanced CT/MRI data for computer-assisted liver surgery planning: Evaluation of state-of-the-art methods. Int J Med Robot Comput Assist Surg 1(3): 6–20

    Article  CAS  Google Scholar 

  46. Christensen G, Geng X, Kuhl J, Bruss J, Grabowski T, Pirwani I et al (2006) Introduction to the non-rigid image registration evaluation project (NIREP). In: WBIR. Lecture notes in computer science, vol 4057. Springer, Berlin, pp 128–135

  47. Lange T, Lamecker H, Hünerbein M, Eulenstein S, Beller S, Schlag PM (2008) Validation metrics for non-rigid registration of medical images containing vessel trees. In: Bildverarbeitung für die Medizin (BVM), pp 82–86

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Lange, T., Papenberg, N., Heldmann, S. et al. 3D ultrasound-CT registration of the liver using combined landmark-intensity information. Int J CARS 4, 79–88 (2009). https://doi.org/10.1007/s11548-008-0270-1

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  • DOI: https://doi.org/10.1007/s11548-008-0270-1

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