Advertisement

A Bayesian Model to Assess \(T_2\) Values and Their Changes Over Time in Quantitative MRI

  • Benoit CombèsEmail author
  • Anne Kerbrat
  • Olivier Commowick
  • Christian Barillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Quantifying \(T_2\) and \(T_2^*\) relaxation times from MRI becomes a standard tool to assess modifications of biological tissues over time or differences between populations. However, due to the relationship between the relaxation time and the associated MR signals such an analysis is subject to error. In this work, we provide a Bayesian analysis of this relationship. More specifically, we build posterior distributions relating the raw (spin or gradient echo) acquisitions and the relaxation time and its modifications over acquisitions. Such an analysis has three main merits. First, it allows to build hierarchical models including prior information and regularisations over voxels. Second, it provides many estimators of the parameters distribution including the mean and the \(\alpha \)-credible intervals. Finally, as credible intervals are available, testing properly whether the relaxation time (or its modification) lies within a certain range with a given credible level is simple. We show the interest of this approach on synthetic datasets and on two real applications in multiple sclerosis.

Keywords

Credible Intervals Multiple Sclerosis Data Ultra-small Superparamagnetic Iron Oxide (USPIO) Normal-appearing White Matter (NAWM) Extended Phase Graph 
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.

References

  1. 1.
    Berger, J.O., et al.: The formal definition of reference priors. Ann. Stat. 37(2), 905–938 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Brooks, S., et al.: Handbook of Markov Chain Monte Carlo. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. CRC Press, Boca Raton (2011)CrossRefGoogle Scholar
  3. 3.
    Commowick, O., et al.: Block-matching strategies for rigid registration of multimodal medical images. In: ISBI, pp. 700–703, May 2012Google Scholar
  4. 4.
    Corot, C., et al.: Recent advances in iron oxide nanocrystal technology for medical imaging. Adv. Drug Deliv. Rev. 58(14), 1471–1504 (2006)CrossRefGoogle Scholar
  5. 5.
    Gelman, A.: Prior distributions for variance parameters in hierarchical models. Bayesian Anal. 1(3), 515–534 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gelman, A., et al.: Why we (usually) don’t have to worry about multiple comparisons. J. Res. Educ. Effectiveness 5(2), 189–211 (2012)CrossRefGoogle Scholar
  7. 7.
    Kjos, B.O., et al.: Reproducibility of T1 and T2 relaxation times calculated from routine mr imaging sequences: phantom study. Am. J. Neuroradiol. 6(2), 277–283 (1985)Google Scholar
  8. 8.
    Kruschke, J.K.: Bayesian assessment of null values via parameter estimation and model comparison. Perspect. Psychol. Sci. 6(3), 299–312 (2011)CrossRefGoogle Scholar
  9. 9.
    Matthias, W.: Extended phase graphs: dephasing, RF pulses, and echoes - pure and simple. J. Magn. Reson. Imaging 41(2), 266–295 (2015)CrossRefGoogle Scholar
  10. 10.
    Milford, D., et al.: Mono-exponential fitting in T2-relaxometry: relevance of offset and first echo. PLoS ONE 10(12), e0145255 (2015). Fan X EditorCrossRefGoogle Scholar
  11. 11.
    Neumann, D., et al.: Simple recipe for accurate T2 quantification with multi spin-echo acquisitions. Magn. Reson. Mater. Phys. Biol. Med. 27(6), 567–577 (2014)CrossRefGoogle Scholar
  12. 12.
    Petrovic, A., et al.: Closed-form solution for T2 mapping with nonideal refocusing of slice selective CPMG sequences. Magn. Reson. Med. 73, 818–827 (2015)CrossRefGoogle Scholar
  13. 13.
    Roberts, G.O., Rosenthal, J.S.: Examples of adaptive MCMC. J. Comput. Graph. Stat. 18(2), 349–367 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Benoit Combès
    • 1
    Email author
  • Anne Kerbrat
    • 2
  • Olivier Commowick
    • 1
  • Christian Barillot
    • 1
  1. 1.Inria, INSERM, VisAGeS U746 Unit/ProjectRennesFrance
  2. 2.Service de NeurologieRennesFrance

Personalised recommendations