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Abstract

We propose a fast algorithm to estimate brain tissue concentrations from conventional T1-weighted images based on a Bayesian maximum a posteriori formulation that extends the “mixel” model developed in the 90’s. A key observation is the necessity to incorporate additional prior constraints to the “mixel” model for the estimation of plausible concentration maps. Experiments on the ADNI standardized dataset show that global and local brain atrophy measures from the proposed algorithm yield enhanced diagnosis testing value than with several widely used soft tissue labeling methods.

Keywords

Hellinger Distance Histogram Mode Partial Volume Estimation Normalize Hippocampus Volume Additional Prior Constraint 
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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexis Roche
    • 1
    • 2
    • 3
  • Florence Forbes
    • 4
    • 5
  1. 1.Siemens Advanced Clinical Imaging TechnologyLausanneSwitzerland
  2. 2.Department of RadiologyCHUVLausanneSwitzerland
  3. 3.Signal Processing Laboratory (LTS5)EPFLLausanneSwitzerland
  4. 4.Mistis ProjectINRIAGrenobleFrance
  5. 5.Laboratoire Jean KuntzmannGrenoble UniversityGrenobleFrance

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