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Multi-Compartment T2 Relaxometry Model Using Gamma Distribution Representations: A Framework for Quantitative Estimation of Brain Tissue Microstructures

Sudhanya Chatterjee 1 Olivier Commowick 1 Simon K. Warfield 2 Christian Barillot 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique , INSERM - Institut National de la Santé et de la Recherche Médicale : U1228
2 CRL - Computational Radiology Laboratory [Boston]
Abstract : Advanced MRI techniques (e.g. – d-MRI, MT, relaxometry etc.) can provide quantitative information of brain tissues. Image voxels are often heterogeneous in terms of microstructure information due to physical limitations and imaging resolution. Quantitative assessment of the brain tissue microstructure can provide valuable insights into neurodegenerative diseases (e.g. - Multiple Sclerosis). In this work, we propose a multicompartment model for T2-Relaxometry to obtain brain microstructure information in a quantitative framework. The proposed method allows simultaneous estimation of the model parameters.
Keywords : Relaxometry MRI Multi-compartment model
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Conference papers
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https://www.hal.inserm.fr/inserm-01543073
Contributor : Sudhanya Chatterjee <>
Submitted on : Tuesday, June 20, 2017 - 2:39:41 PM
Last modification on : Saturday, July 11, 2020 - 3:15:42 AM

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  • HAL Id : inserm-01543073, version 1

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CNRS | UNIV-RENNES1 | INRIA | IRISA | UR1-UFR-ISTIC | UNIV-RENNES | INSA-RENNES

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Sudhanya Chatterjee, Olivier Commowick, Simon K. Warfield, Christian Barillot. Multi-Compartment T2 Relaxometry Model Using Gamma Distribution Representations: A Framework for Quantitative Estimation of Brain Tissue Microstructures. ISMRM 25TH ANNUAL MEETING & EXHIBITION, Apr 2017, Honolulu, Hawaii, USA, United States. ⟨inserm-01543073⟩

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