Quantitative Body DW-MRI Biomarkers Uncertainty Estimation Using Unscented Wild-Bootstrap

  • M. Freiman
  • S. D. Voss
  • R. V. Mulkern
  • J. M. Perez-Rossello
  • S. K. Warfield
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)


We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of ~36% in the uncertainty values.


Uncertainty Estimation Unscented Transform Model Parameter Uncertainty Repeated Acquisition Intravoxel Incoherent Motion 
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  1. 1.
    Bihan, D.L., Breton, E., Lallemand, D., Aubin, M.L., Vignaud, J., Laval-Jeantet, M.: Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168(2), 497–505 (1988)CrossRefGoogle Scholar
  2. 2.
    Chandarana, H., Lee, V., Hecht, E., Taouli, B., Sigmund, E.: Comparison of Biexponential and Monoexponential Model of Diffusion Weighted Imaging in Evaluation of Renal Lesions: Preliminary Experience. Invest Radiol. 46(5), 285–291 (2010)Google Scholar
  3. 3.
    Chung, S., Lu, Y., Henry, R.G.: Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33(2), 531–541 (2006)CrossRefGoogle Scholar
  4. 4.
    Davidson, R., Flachaire, E.: The wild bootstrap, tamed at last. J. of Econometrics 146(1), 162–169 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Dudeck, O., Zeile, M., Pink, D., Pech, M., Tunn, P., Reichardt, P., Ludwig, W., Hamm, B.: Diffusion-weighted magnetic resonance imaging allows monitoring of anticancer treatment effects in patients with soft-tissue sarcomas. J. Magn. Reson. Imaging 27(5), 1109–1113 (2008)CrossRefGoogle Scholar
  6. 6.
    Eccles, C., Haider, E., Haider, M., Fung, S., Lockwood, G., Dawson, L.: Change in diffusion weighted mri during liver cancer radiotherapy: preliminary observations. Acta Oncol. 48(7), 1034–1043 (2009)CrossRefGoogle Scholar
  7. 7.
    Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall, New York (1993)CrossRefzbMATHGoogle Scholar
  8. 8.
    Fujimoto, K., Tonan, T., Azuma, S., Kage, M., Nakashima, O., Johkoh, T., Hayabuchi, N., Okuda, K., Kawaguchi, T., Sata, M., Qayyum, A.: Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology 258(3), 739–748 (2011)CrossRefGoogle Scholar
  9. 9.
    Gahr, N., Darge, K., Hahn, G., Kreher, B., von Buiren, M., Uhl, M.: Diffusion-weighted MRI for differentiation of neuroblastoma and ganglioneuroblastoma/ganglioneuroma. Eur. J. Radiol (2010) (in press)Google Scholar
  10. 10.
    Galbán, C., Chenevert, T., Meyer, C., Tsien, C., Lawrence, T., Hamstra, D., Junck, L., Sundgren, P., Johnson, T., Ross, D., Rehemtulla, A., Ross, B.: The parametric response map is an imaging biomarker for early cancer treatment outcome. Nat. Med. 15(5), 572–576 (2009)CrossRefGoogle Scholar
  11. 11.
    Julier, S., Uhlmann, J.: Unscented filtering and nonlinear estimation. Proc. of the IEEE 92(3), 401–422 (2004)CrossRefGoogle Scholar
  12. 12.
    Koh, D.M., Collins, D.J., Orton, M.R.: Intravoxel incoherent motion in body diffusion-weighted mri: Reality and challenges. AJR Am. J. Roentgenol. 196(6), 1351–1361 (2011)CrossRefGoogle Scholar
  13. 13.
    Koh, D.M., Thoeny, H.C., Chenevert, T.L.: Principles of Diffusion-Weighted Imaging (DW-MRI) as Applied to Body Imaging. In: Diffusion-Weighted MR Imaging. In: Medical Radiology. Springer, Heidelberg (2010)Google Scholar
  14. 14.
    Perazella, M.A.: Current status of gadolinium toxicity in patients with kidney disease. Clin. J. Am. Soc. Nephrol. 4(2), 461–469 (2009)CrossRefGoogle Scholar
  15. 15.
    Powell, M.: The BOBYQA algorithm for bound constrained optimization without derivatives. technical report NA2009/06, Dep. App. Math. and Th. Physics, Cambridge, England (2009)Google Scholar
  16. 16.
    Scherrer, B., Warfield, S.: Toward an accurate multi-fiber assessment strategy for clinical practice. In: ISBI 2011 (2011)Google Scholar
  17. 17.
    Xu, Y., Wang, X., Jiang, X.: Relationship between the renal apparent diffusion coefficient and glomerular filtration rate: preliminary experience. J. Magn. Reson. Imaging 26(3), 678–681 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Freiman
    • 1
  • S. D. Voss
    • 2
  • R. V. Mulkern
    • 2
  • J. M. Perez-Rossello
    • 2
  • S. K. Warfield
    • 1
  1. 1.Computational Radiology Laboratory, Children’s HospitalHarvard Medical SchoolBostonUSA
  2. 2.Department of Radiology, Children’s HospitalHarvard Medical SchoolBostonUSA

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