Skip to main content

Intensity-Based MRI to X-ray Mammography Registration with an Integrated Fast Biomechanical Transformation

  • Conference paper
Breast Imaging (IWDM 2012)

Abstract

Determining MRI to X-ray mammography correspondence is a clinically useful task that is challenging for radiologists due to the large deformation that the breast undergoes. In this work we propose an intensity-based registration framework with a new integrated transformation module that uses a biomechanical model of the breast in order to simulate the mammographic compression. The breast model is patient-specific and is extracted from the MRI of the patient. The transformation model has seven degrees of freedom and uses a fast explicit Finite Element (FE) solver that runs on the graphics card, enabling it to be fully integrated into the optimisation scheme. The iteratively updated parameters include both parameters of the biomechanical model simulation, and also rigid transformation parameters of the breast geometry model. The framework was tested on five clinical cases. The mean registration error was 7.6±2.4mm for the CC and 10.2±2.3mm for the MLO view registrations, indicating that this could be a useful clinical tool.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Behrenbruch, C., Marias, K., Armitage, P., Moore, N., English, R., Clarke, J., Brady, M.: Fusion of contrast-enhanced breast MR and mammographic imaging data. Medical Image Analysis 7, 311–340 (2003)

    Article  Google Scholar 

  2. Marti, R., Zwiggelaar, R., Rubin, C., Denton, E.: 2D-3D correspondence in mammography. Cybernetics and Systems 35, 85–105 (2004)

    Google Scholar 

  3. Ruiter, N., Stotzka, R., Muller, T., Gemmeke, H., Reichenbach, J., Kaiser, W.: Model-Based registration of X-ray Mammograms and MR images of the female breast. IEEE Transactions on Nuclear Science 53, 204–211 (2006)

    Article  Google Scholar 

  4. Lee, A., Rajagopal, V., Reynolds, H., Doyle, A., Nielsen, P., Nash, M.: Breast X-ray and MR image fusion using Finite Element Modeling. In: MICCAI Workshop on Breast Image Analysis, pp. 129–136 (2011)

    Google Scholar 

  5. Mertzanidou, T., Hipwell, J., Cardoso, M., Zhang, X., Tanner, C., Ourselin, S., Bick, U., Huisman, H., Karssemeijer, N., Hawkes, D.: MRI to X-ray mammography registration using a volume-preserving affine transformation. Medical Image Analysis (in press, 2012)

    Google Scholar 

  6. Mertzanidou, T., Hipwell, J., Han, L., Huisman, H., Karssemeijer, N., Hawkes, D.: MRI to X-ray mammography registration using an ellipsoidal breast model and biomechanically simulated compressions. In: MICCAI Workshop on Breast Image Analysis, pp. 161–168 (2011)

    Google Scholar 

  7. Taylor, Z., Comas, O., Cheng, M., Passenger, J., Hawkes, D., Atkinson, D., Ourselin, S.: On modelling of anisotropic viscoelasticity for soft tissue simulation: Numerical solution and GPU execution. Medical Image Analysis 13, 234–244 (2009)

    Article  Google Scholar 

  8. Han, L., Hipwell, J., Tanner, C., Taylor, Z., Mertzanidou, T., Ourselin, S., Hawkes, D.: Development of patient-specifc biomechanical models for predicting large breast deformation. Physics in Medicine and Biology 57, 455–472 (2012)

    Article  Google Scholar 

  9. Tanner, C., White, M., Guarino, S., Hall-Craggs, M., Douek, M., Hawkes, D.: Large breast compressions – Observations and evaluation of simulations. Medical Physics 38, 682–690 (2011)

    Article  Google Scholar 

  10. The Insight Segmentation and Registration Toolkit (ITK), http://www.itk.org

  11. Hopp, T., Baltzer, P., Dietzel, M., Kaiser, W., Ruiter, N.: 2D/3D image fusion of X-ray mammograms with breast MRI: visualizing dynamic contrast enhancement in mammograms. Int. Journal of Computer Assisted Radiology and Surgery (2011) (in press)

    Google Scholar 

  12. Gubern-Merida, A., Kallenberg, M., Marti, R., Karssemeijer, N.: Fully automatic fibroglandular tissue segmentation in breast MRI: atlas-based approach. In: MICCAI Workshop on Breast Image Analysis, pp. 73–80 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mertzanidou, T. et al. (2012). Intensity-Based MRI to X-ray Mammography Registration with an Integrated Fast Biomechanical Transformation. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31271-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31270-0

  • Online ISBN: 978-3-642-31271-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics