A Compressed Sensing Approach for MR Tissue Contrast Synthesis

  • Snehashis Roy
  • Aaron Carass
  • Jerry Prince
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6801)


The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multi-scanner neuroimaging studies.


compressed sensing magnetic resonance imaging (MRI) image synthesis phantom standardization segmentation intensity normalization histogram matching histogram equalization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bazin, P.L., Pham, D.L.: Topology-preserving tissue classification of magnetic resonance brain images. IEEE Trans. on Medical Imaging 26(4), 487–496 (2007)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C.: A Convergence Theorem for the Fuzzy ISO-DATA Clustering Algorithms. IEEE Trans. on Pattern Anal. Machine Intell. 20(1), 1–8 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Candes, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. on Pure and Appl. Math. 59(8), 1207–1223 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Christensen, J.D.: Normalization of brain magnetic resonance images using histogram even-order derivative analysis. Mag. Res. Imaging 21(7), 817–820 (2003)CrossRefGoogle Scholar
  5. 5.
    Clark, K.A., Woods, R.P., Rottenber, D.A., Toga, A.W., Mazziotta, J.C.: Impact of acquisition protocols and processing streams on tissue segmentation of T1 weighted MR images. NeuroImage 29(1), 185–202 (2006)CrossRefGoogle Scholar
  6. 6.
    Cocosco, C.A., Kollokian, V., Kwan, R.K.S., Evans, A.C.: BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. NeuroImage 5(4), S425 (1997), Google Scholar
  7. 7.
    Deichmann, R., Good, C.D., Josephs, O., Ashburner, J., Turner, R.: Optimization of 3-D MP-RAGE Sequences for Structural Brain Imaging. NeuroImage 12(3), 112–127 (2000)CrossRefGoogle Scholar
  8. 8.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of Royal Stat. Soc. 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Elad, M., Bruckstein, A.M.: A Generalized Uncertainty Principle and Sparse Representation in Pairs of Bases. IEEE Trans. Inf. Theory 48(9), 2558–2567 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Fischl, B., Salat, D.H., van der Kouwe, A.J.W., Makris, N., Segonne, F., Quinn, B.T., Dale, A.M.: Sequence-independent segmentation of magnetic resonance images. NeuroImage 23(1), 69–84 (2004)CrossRefGoogle Scholar
  12. 12.
    Friedman, L., Stern, H., Brown, G.G., Mathalon, D.H., Turner, J., Glover, G.H., Gollub, R.L., Lauriello, J., Lim, K.O., Cannon, T., Greve, D.N., Bockholt, H.J., Belger, A., Mueller, B., Doty, M.J., He, J., Wells, W., Smyth, P., Pieper, S., Kim, S., Kubicki, M., Vangel, M., Potkin, S.G.: Test-Retest and Between-Site Reliability in a Multicenter fMRI Study. Human Brain Mapping 29(8), 958–972 (2008)CrossRefGoogle Scholar
  13. 13.
    Han, X., Fischl, B.: Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms. IEEE Trans. Med. Imag. 26(4), 479–486 (2007)CrossRefGoogle Scholar
  14. 14.
    He, R., Datta, S., Tao, G., Narayana, P.A.: Information measures-based intensity standardization of MRI. In: Intl. Conf. Engg. in Med. and Biology Soc., pp. 2233–2236 (August 2008)Google Scholar
  15. 15.
    Jager, F., Nyul, L., Frericks, B., Wacker, F., Hornegger, J.: Whole Body MRI Intensity Standardization. In: Bildverarbeitung für die Medizin 2008. Informatik aktuell, ch. 20, Springer, Heidelberg (2007)Google Scholar
  16. 16.
    Kawas, C., Gary, S., Brookmeyer, R., Fozard, J., Zonderman, A.: Age-specific incidence rates of Alzheimer’s disease: the Baltimore Longitudinal Study of Aging. Neurology 54(11), 2072–2077 (2000)CrossRefGoogle Scholar
  17. 17.
    Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated Model-Based Tnumber Classification of MR Images of the Brain. IEEE Trans. on Med. Imag. 18(10), 897–908 (1999)CrossRefGoogle Scholar
  18. 18.
    Madabhushi, A., Udupa, J.K.: New methods of MR image intensity standardization via generalized scale. Med. Phys. 33(9), 3426–3434 (2006)CrossRefGoogle Scholar
  19. 19.
    Nyul, L.G., Udupa, J.K.: On Standardizing the MR Image Intensity Scale. Mag. Res. in Medicine 42(6), 1072–1081 (1999)CrossRefGoogle Scholar
  20. 20.
    Osborne, M.R., Presnell, B., Turlach, B.A.: A new approach to variable selection in least squares problems. IMA J. Numerical Analysis 20(3), 389–403 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Prince, J.L., Tan, Q., Pham, D.L.: Optimization of MR Pulse Sequences for Bayesian Image Segmentation. Medical Physics 22(10), 1651–1656 (1995)CrossRefGoogle Scholar
  22. 22.
    Rohde, G.K., Aldroubi, A., Dawant, B.M.: The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Trans. on Med. Imag. 22, 1470–1479 (2003)CrossRefGoogle Scholar
  23. 23.
    Tibshirani, R.: Regression Shrinkage and Selection via the Lasso. J. Royal Stat. Soc. 58(1), 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory 53, 4655–4666 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Weisenfeld, N.I., Warfield, S.K.: Normalization of Joint Image-Intensity Statistics in MRI Using the Kullback-Leibler Divergence. In: Intl. Symp. on Biomed. Imag (ISBI), vol. 1, pp. 101–104 (April 2004)Google Scholar
  26. 26.
    Yang, J., Wright, J., Huang, T., Ma, Y.: Image Super-Resolution Via Sparse Representation. IEEE Trans. Image. Proc. 19(11), 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Snehashis Roy
    • 1
  • Aaron Carass
    • 1
  • Jerry Prince
    • 1
  1. 1.Image Analysis and Communication Laboratory, Dept. of Electrical and Computer Engg.The Johns Hopkins UniversityUSA

Personalised recommendations