Advertisement

Generalized Filtered Back-Projection Reconstruction in Breast Tomosynthesis

  • Bernhard E. H. Claus
  • Jeffrey W. Eberhard
  • Andrea Schmitz
  • Paul Carson
  • Mitchell Goodsitt
  • Heang-Ping Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

Abstract

Tomosynthesis reconstruction that produces high-quality images is a difficult problem, due mainly to the highly incomplete data. In this work we present a motivation for the generalized filtered backprojection (GFBP) approach to tomosynthesis reconstruction. This approach is fast (since non-iterative), flexible, and results in reconstructions with an image quality that is similar or superior to reconstructions that are mathematically optimal. Results based on synthetic data and patient data are presented.

Keywords

Image Quality Reconstruction Algorithm Projection Image Digital Mammography Reconstruction Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wu, T., Moore, R., Rafferty, E., Kopans, D.: A comparison of reconstruction algorithms for breast tomosynthesis. Med. Phys. 31(9), 2636–2647 (2004)CrossRefGoogle Scholar
  2. 2.
    Claus, B.E.H., Eberhard, J.W., Thomas, J.A., Galbo, C.E., Pakenas, W.P., Muller, S.L.: Preference Study of Reconstructed Image Quality in Mammographic Tomosynthesis. In: Peitgen, H.-O. (ed.) Digital Mammography IWDM 2002, Proceedings of the 6th IWDM, Bremen, Germany. Springer, Heidelberg (2003)Google Scholar
  3. 3.
    Claus, B.E.H., Eberhard, J.W.: A New Method for 3D Reconstruction in Digital Tomosynthesis. In: Proc. SPIE, Medical Imaging 2002, vol. 4684 (2002)Google Scholar
  4. 4.
    Suryanarayanan, S., Karellas, A., Vedantham, S., Baker, S.P., Glick, S.J., D’Orsi, C.J., Webber, R.L.: Evaluation of Linear and Nonlinear Tomosynthetic Reconstruction Methods in Digital Mammography. Acad. Radiol. 8, 219–224 (2000)CrossRefGoogle Scholar
  5. 5.
    Suryanarayanan, S., Karellas, A., Vedantham, S., Glick, S.J., D’Orsi, C.J., Baker, S.P., Webber, R.L.: Comparison of Tomosynthesis Methods Used in Digital Mammography. Acad. Radiol. 7, 1085–1097 (2000)CrossRefGoogle Scholar
  6. 6.
    Wu, T., Stewart, A., Stanton, M., Phillips, W., McCauley, T., Kopans, D.B., Moore, R.H., Eberhard, J.W., Opsahl-Ong, B., Niklason, L., Williams, M.B.: Tomographic mammography using a limited number of low-dose cone-beam projection images. Med. Phys. 30(3), 365–380 (2003)CrossRefGoogle Scholar
  7. 7.
    Wang, B., Barner, K., Lee, D.: Algebraic Tomosynthesis Reconstruction. In: Proc. SPIE, Medical Imaging 2004, vol. 5370, pp. 711–718 (2004)Google Scholar
  8. 8.
    Dobbins, J.T., Godfrey, D.J.: Digital x-ray tomosynthesis: current state of the art and clinical potential. Phys. Med. Biol. 48, R65–R106 (2003)CrossRefGoogle Scholar
  9. 9.
    Eberhard, J.W., Albagli, D., Schmitz, A., Claus, B.E.H., Carson, P., Goodsitt, M., Chan, H.-P., Roubidoux, M., Thomas, J.A., Osland, J.: Mammography Tomosynthesis System for High- Performance 3D Imaging. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 137–143. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Byng, J.W., Critten, J.P., Yaffe, M.J.: Thickness-equalization processing for mammographic images. Radiology 203, 564–568 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bernhard E. H. Claus
    • 1
  • Jeffrey W. Eberhard
    • 1
  • Andrea Schmitz
    • 1
  • Paul Carson
    • 2
  • Mitchell Goodsitt
    • 2
  • Heang-Ping Chan
    • 2
  1. 1.One Research CircleGE Global ResearchNiskayuna
  2. 2.University of MichiganAnn Arbor

Personalised recommendations