A Bayesian Model to Assess \(T_2\) Values and Their Changes Over Time in Quantitative MRI
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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.
KeywordsCredible Intervals Multiple Sclerosis Data Ultra-small Superparamagnetic Iron Oxide (USPIO) Normal-appearing White Matter (NAWM) Extended Phase Graph
- 3.Commowick, O., et al.: Block-matching strategies for rigid registration of multimodal medical images. In: ISBI, pp. 700–703, May 2012Google Scholar
- 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
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