DCE-MRI Analysis Using Sparse Adaptive Representations

  • Gabriele Chiusano
  • Alessandra Staglianò
  • Curzio Basso
  • Alessandro Verri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)


Dynamic contrast-enhanced MRI (DCE-MRI) plays an important role as an imaging method for the diagnosis and evaluation of several diseases. Indeed, clinically relevant, per-voxel quantitative information may be extracted through the analysis of the enhanced MR signal. This paper presents a method for the automated analysis of DCE-MRI data that works by decomposing the enhancement curves as sparse linear combinations of elementary curves learned without supervision from the data. Experimental results show that performances in denoising and unsupervised segmentation improve over parametric methods.


Juvenile Idiopathic Arthritis Sparse Representation Sparse Code Dictionary Learning Unsupervised Segmentation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agner, S., et al.: Segmentation and classification of triple negative breast cancers using DCE-MRI. In: Proc. IEEE ISBI 2009, pp. 1227–1230 (2009)Google Scholar
  2. 2.
    Aharon, M., et al.: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11) (2006)Google Scholar
  3. 3.
    Alonzi, R., Padhani, A.R., Allen, C.: Dynamic contrast enhanced MRI in prostate cancer. Eur. J. Radiol. 63(3), 335–350 (2007)CrossRefGoogle Scholar
  4. 4.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  5. 5.
    Crum, W., et al.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE TMI 25(11), 1451–1461 (2006)Google Scholar
  6. 6.
    Damasio, M.B., Malattia, C., Martini, A., Tomà, P.: Synovial and inflammatory diseases in childhood: role of new imaging modalities in the assessment of patients with juvenile idiopathic arthritis. Pediatric Radiology 40(6), 985–998 (2010)CrossRefGoogle Scholar
  7. 7.
    Guo, J., Reddick, W.: DCE-MRI pixel-by-pixel quantitative curve pattern analysis and its application to osteosarcoma. Journal of MR 30(1), 177–184 (2009)Google Scholar
  8. 8.
    Harris, N., Gauden, V., Fraser, P., Williams, S., Parker, G.: MRI measurement of blood-brain barrier permeability following spontaneous reperfusion in the starch microsphere model of ischemia. Magnetic Resonance Imaging 20(3), 221–230 (2002)CrossRefGoogle Scholar
  9. 9.
    Kubassova, O., Boesen, M., Boyle, R.D., Cimmino, M.A., Jensen, K.E., Bliddal, H., Radjenovic, A.: Fast and robust analysis of dynamic contrast enhanced MRI datasets. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 261–269. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Lavini, C., et al.: Pixel-by-pixel analysis of DCE MRI curve patterns and an illustration of its application to the imaging of the musculoskeletal system. Magnetic Resonance Imaging 25(5), 604–612 (2007)CrossRefGoogle Scholar
  11. 11.
    Lee, H., Battle, A., Raina, R., Ng, A.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems, NIPS 2006, vol. 19 (2006)Google Scholar
  12. 12.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research 11, 19–60 (2010)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Olshausen, B., Field, D.: Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research 37(23), 3311–3325 (1997)CrossRefGoogle Scholar
  14. 14.
    Schmid, V.J., et al.: Quantitative analysis of dynamic contrast-enhanced MR images based on bayesian p-splines. IEEE TMI 28(6), 789–798 (2009)Google Scholar
  15. 15.
    Staglianò, A., Chiusano, G., Basso, C., Santoro, M.: Learning adaptive and sparse representations of medical images. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 130–140. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Vivier, P., Blondiaux, E., Dolores, M., Marouteau-Pasquier, N., Brasseur, M., Petitjean, C., Dacher, J.: Functional mr urography in children. J. Radiol. (2009)Google Scholar
  17. 17.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801 (June 2009)Google Scholar
  18. 18.
    Zöllner, F.G., et al.: Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Computerized Medical Imaging and Graphics 33(3), 171–181 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gabriele Chiusano
    • 1
  • Alessandra Staglianò
    • 1
  • Curzio Basso
    • 1
  • Alessandro Verri
    • 1
  1. 1.DISI, Università di GenovaGenovaItaly

Personalised recommendations