Abstract
Objective
A novel 3-D/2-D registration method based on matching 3-D pre-interventional image gradients and coarsely reconstructed 3-D gradients from intra-interventional 2-D images is presented.
Material and methods
The novel method establishes correspondences between two sets of gradients by searching for correspondences along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was evaluated by the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography (CT), magnetic resonance (MR), and 2-D X-ray images of two spine segments, and evaluation criteria.
Results
Preliminary results show significant improve- ment in robustness (capture range and success rate) over three well established intensity-based, gradient-based, and reconstruction-based methods.
Conclusion
The 3-D/2-D gradient reconstruction-based registration method efficiently combines the advantages of gradient and reconstruction-based methods, thereby enabling robust registration of CT and MR to only two X-ray images, while keeping the computational demands low.
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References
Lavallee S, Szeliski R (1995) Recovering the position and orientation of free-form objects from image contours using 3D distance maps. IEEE Trans Pattern Anal Mach Intell 17: 378–390
Feldmar J, Ayache N, Betting F (1997) 3D-2D projective registration of free-form curves and surfaces. Comput Vis Image Underst 65: 403–424
Zheng G, Dong X, Rajamani KT et al (2007) Accurate and robust reconstruction of a surface model of the proximal femur from sparse point data and a dense point distribution model for surgical navigation. IEEE Trans Biomed Eng 54: 2109–2122
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: 1749–1760
Khamene A, Bloch P, Wein W et al (2006) Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy. Med Image Anal 10: 96–112
Kim J, Li SD, Pradhan D et al (2007) Comparison of similarity measures for rigid-body CT/Dual X-ray image registrations. Technol Cancer Res Treat 6: 337–345
Penney GP, Weese J, Little JA et al (1998) A comparison of similarity measures for use in 2-D–3-D medical image registration. IEEE Trans Med Imaging 17: 586–595
Tomaževič D, Likar B, Slivnik T et al (2003) 3-D/2-D registration of CT and MR to X-ray images. IEEE Trans Med Imaging 22: 1407–1416
Livyatan H, Yaniv Z, Joskowicz L (2003) Gradient-based 2-D/3-D rigid registration of fluoroscopic X-ray to CT. IEEE Trans Med Imaging 22: 1395–1406
Tomaževič D, Likar B, Pernuš F (2006) 3-D/2-D registration by integrating 2-D information in 3-D. IEEE Trans Med Imaging 25: 17–27
Prümmer M, Hornegger J, Pfister M et al (2006) Multi-modal 2D–3D non-rigid registration. In: Reinhardt JM, Pluim JP (ed) Proceedings of SPIE medical imaging 2006: image processing, vol 6144
Fischler MA, Bolles RC (1981) Random sample consensus—a paradigm for model-fitting with applications to image-analysis and automated cartography. Commun Acm 24: 381–395
van de Kraats EB, Penney GP, Tomaževič D et al (2005) Standardized evaluation methodology for 2-D–3-D registration. IEEE Trans Med Imaging 24: 1177–1189
Press WH, Teukolsky SA, Vetterling WT et al (1992) Numerical recipes in C++. Cambridge University Press, Cambridge
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE T Pattern Anal 14: 239–256
Markelj P, Tomaževič D, Pernuš F et al (2007) Optimizing bone extraction in MR images for 3D/2D gradient based registration of MR and X-ray images. In: Reinhardt JM, Pluim JP (ed) Proceedings of medical imaging 2007: image processing, vol 6512
Maes F, Collignon A, Vandermeulen D et al (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16: 187–198
Russakoff DB, Rohlfing T, Mori K et al (2005) Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D–3D image registration. IEEE Trans Med Imaging 24: 1441–1454
Romanelli P, Schaal DW, Adler JR (2006) Image-guided radiosurgical ablation of intra- and extra-cranial lesions. Technol Cancer Res Treat 5: 421–428
Chui HL, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Comput Vis Image Und 89: 114–141
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Markelj, P., Tomaževič, D., Pernuš, F. et al. Robust 3-D/2-D registration of CT and MR to X-ray images based on gradient reconstruction. Int J CARS 3, 477–483 (2008). https://doi.org/10.1007/s11548-008-0244-3
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DOI: https://doi.org/10.1007/s11548-008-0244-3