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

  • Benoit Combès
  • 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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

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

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