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A Conditional Autoregressive Model for Estimating Slow and Fast Diffusion from Magnetic Resonance Images

  • Ettore LanzaroneEmail author
  • Elisa Scalco
  • Alfonso Mastropietro
  • Simona Marzi
  • Giovanna Rizzo
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 296)

Abstract

The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion parameters of water molecules in biological tissues, which are used as biomarkers for different diseases. However, the standard approach to obtain the maps of these parameters is based on a voxel-by-voxel estimation and neglects the spatial correlations, thus resulting in noisy maps. To get better maps, we propose a Bayesian approach that exploits a Conditional Autoregressive (CAR) prior density. We consider a pure CAR model and a mixture CAR model, and we compare the outcomes with two benchmark approaches. Results show better maps under the CAR models.

Keywords

Conditional autoregressive model Diffusion parameters Intra-voxel incoherent motion Magnetic resonance imaging Spatial correlation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ettore Lanzarone
    • 1
    Email author
  • Elisa Scalco
    • 2
  • Alfonso Mastropietro
    • 2
  • Simona Marzi
    • 3
  • Giovanna Rizzo
    • 2
  1. 1.Institute for Applied Mathematics and Information Technologies (IMATI), CNRMilanItaly
  2. 2.Institute of Molecular Bioimaging and Physiology (IBFM), CNRLeccoItaly
  3. 3.Medical Physics Laboratory, Regina Elena National Cancer InstituteRomeItaly

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