Partial volume estimation in brain MRI revisited

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.
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Communication dans un congrès
Polina Golland; Nobuhiko Hata; Christian Barillot; Joachim Hornegger; Robert Howe. MICCAI 2014 - 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2014, Boston, United States. Springer, Lecture Notes in Computer Science, 8673, pp.771-778, <10.1007/978-3-319-10404-1_96>
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https://hal.inria.fr/hal-01107469
Contributeur : Florence Forbes <>
Soumis le : mardi 20 janvier 2015 - 17:40:46
Dernière modification le : mercredi 14 décembre 2016 - 01:07:24

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Alexis Roche, Florence Forbes. Partial volume estimation in brain MRI revisited. Polina Golland; Nobuhiko Hata; Christian Barillot; Joachim Hornegger; Robert Howe. MICCAI 2014 - 17th International Conference on Medical Image Computing and Computer Assisted Intervention, Sep 2014, Boston, United States. Springer, Lecture Notes in Computer Science, 8673, pp.771-778, <10.1007/978-3-319-10404-1_96>. <hal-01107469>

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