Abstract
3D reconstruction from image data is required in many medical procedures. Recently, the use of fluoroscopy data to generate these 3D models has been explored. Most existing methods require knowledge of the scanning path either from precise hardware, or pre-calibration procedures. We propose an alternative of obtaining this needed pose information without the need of additional hardware or pre-calibration so that many existing fluoroscopes can be used.
Our method generates 3D data from fluoroscopy collected along a non-repeatable path using cone-beam tomographic reconstruction techniques. The novelty of our approach is its application to imagery from existing fluoroscopic systems that are not instrumented to generate pose information or collect data along specific paths. Our method does not require additional hardware to obtain the pose, but instead gathers the needed object to camera pose information for each frame using 2D to 3D model matching techniques [1-3]. Metallic markers are attached to the object being imaged to provide features for pose determination. Given the pose, we apply Grangeat’s cone-beam reconstruction algorithm to recover the 3D data.
In developing this approach, several problems arose that have not been addressed previously in the literature. First, because the Radon space sampling is different for each scan, we cannot to take advantage of a known Radon space discretization. Therefore we have developed a matching score that will give the best Radon plane match for the resampling step in Grangeat’s approach [4]. Second, although we assume Tuy’s condition [5] is satisfied, there are sometimes data gaps due to discretization. We have developed a method to correct for these gaps in the Radon data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kall, B.K.: Comprehensive multimodality surgical planning and interactive neurosurgery. In: Kelly, P.J., Kall, B.K. (eds.) Computers in Stereotactic Neruosurgery, pp. 209–229. Blackwell Scientific, Boston (1992)
Penney, G., et al.: A comparison of similarity measures for use in 2D-3D medical image registration. IEEE Trans. on Medical Imaging 1496, 586–595 (1998)
Weese, J., et al.: Voxel-based 2D/3D registration of fluoroscopy images and CT scans for image-guided surgery. IEEE Trans. Information Technology in Biomedicine 1(4), 284–293 (1997)
Grangeat, P.: Mathematical Framework of Cone Beam 3D Reconstruction via the First Derivative of the Radon Transform. In: Mathematical Methods in Tomography, p. 66. Springer, Heidelberg (1992)
Tuy, H.K.: INVERSION FORMULA FOR CONE-BEAM RECONSTRUCTION. SIAM Journal on Applied Mathematics 43(3), 546–552 (1983)
Mitschke, M., Navab, N.: Recovering the X-ray projection geometry for three-dimensional tomographic reconstruction with additional sensors: Attached camera versus external navigation system. Medical Image Analysis 7, 65–78 (2003)
Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. Journal Optical Soc. of America 4(4), 629–642 (1987)
Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. Journal of the Optical Society of America A: Optics and Image Science 1(6), 612–619 (1984)
Mueller, K., Yagel, R., Wheller, J.J.: Anti-aliased three-dimensional cone-beam reconstruction of low-constrast objects with algebraic methods. IEEE Trans. on Medical Imaging 519, 519–537 (1999)
Mueller, K., Yagel, R., Wheller, J.J.: Fast implementations of algebraic methods for three-dimensional reconstruction from cone-beam data. IEEE Trans. on Medical Imaging 18(6), 538–548 (1999)
Noo, F., Defrise, M., Clack, R.: Direct reconstruction of cone-beam data acquired with a vertex path containing a circle. IEEE Trans. on Image Processing 7(6), 854–867 (1998)
Schaller, S., Flohr, T., Steffen, P.: Efficient Fourier method for 3-D Radon inversion in exact cone-beam CT reconstruction. IEEE Transactions on Medical Imaging 17(2), 244–250 (1998)
Axelsson, C., Danielsson, P.E.: Three-dimensional reconstruction from cone-beam data in O(N3̂log N) time. Physics in Medicine and Biology 39(3), 477 (1994)
Jacobson, C.: Fourier Methods in 3D-Reconstruction from Cone-Beam Data. Linkoping Studies in Science and Technologry Dissertations 427 (1996)
Kudo, H., et al.: Performance of Quasi-Exact Cone-Beam Filtered Backprojection Algorithm for Axially Truncated Helical Data. IEEE Transactions on Nuclear Science 46(3), 608–617 (1999)
Turbell, H., Per-Erik, D.: Helical cone-beam tomography. International Journal of Imaging Systems and Technology 11(1), 91–100 (2000)
Wang, G., et al.: A General Cone-Beam Reconstruction Algorithm. IEEE Transactions on Medical Imaging 12(3), 486–496 (1993)
Wang, X., Ning, R.: Cone-beam reconstruction algorithm for circle-plus-arc data-acquisition geometry. IEEE Transactions on Medical Imaging 18(9), 815–824 (1999)
Noo, F., Clack, R., Defrise, M.: Cone-beam Reconstruction from General Discrete Vertex Sets using Rdon Rebinning Algorithms. IEEE Transactions on Nuclear Science 44(3), 1309–1316 (1997)
Baker, C.: Computed Tomography from Imagery Generated by Fluoroscopy along an Arbitrary Path, in Engineering. In: Golden. Colorado School of Mines, p.115 (2004)
Grimson, W.E.L.: Object recognition by Computer. MIT Press, Cambridge (1990)
Haralick, R., Shapiro, L.: Computer and Robot Vision. Addison-Wesley Inc. Reading (1993)
Kudo, H., Saito, T.: Derivation and implementation of a cone-beam reconstruction algorithm for nonplanar orbits. IEEE Transactions on Medical Imaging 13(1), 196–211 (1994)
Hsieh, J.: Reconstruction Algorithm for Single Circular Orbit Cone Beam Scans, pp. 836–838. IEEE, Los Alamitos (2002)
Benac, J.: Alternating Minimization Algorithms for Metal Artifact Reduction in Transmission Tomography in Electrical Engineering. Washington University, St. Louis (2002)
Wang, G., et al.: Iterative Deblurring for CT Metal Artifact Reduction. IEEE Transactions on Medical Imaging 15(5), 657–664 (1996)
Zhao, S., et al.: X-Ray CT Metal Artifact Reduction Using Wavelets: An Application for Imaging Total Hip Prostheses. IEEE Transactions on Medical Imaging 19(12) (2000)
Jolesz, F.K., Shtern, F.: The Vision of Image-Guided Computer Surgery. In: Taylor, R. (ed.) The High Tech Operating Room in Computer Integrated Surgery - Technology and Clinical Applications, pp. 717–721. MIT Press, Cambridge (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Baker, C., Debrunner, C., Mahfouz, M., Hoff, W., Bowen, J. (2004). CT from an Unmodified Standard Fluoroscopy Machine Using a Non-reproducible Path. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-27816-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22675-8
Online ISBN: 978-3-540-27816-0
eBook Packages: Springer Book Archive