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A Bayesian Model to Assess T2 Values and Their Changes Over Time in Quantitative MRI

Benoit Combès 1, * Anne Kerbrat 2 Olivier Commowick 1 Christian Barillot 1 
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Quantifying T2 and T2* 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 α-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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Submitted on : Thursday, November 10, 2016 - 10:00:31 AM
Last modification on : Wednesday, February 2, 2022 - 3:55:04 PM
Long-term archiving on: : Wednesday, March 15, 2017 - 4:01:53 AM


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Benoit Combès, Anne Kerbrat, Olivier Commowick, Christian Barillot. A Bayesian Model to Assess T2 Values and Their Changes Over Time in Quantitative MRI. 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct 2016, Athens, Greece. pp.570 - 578, ⟨10.1007/978-3-319-46726-9_66⟩. ⟨inserm-01349557⟩



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