HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Multi-Compartment Model of Brain Tissues from T2 Relaxometry MRI Using Gamma Distribution

Sudhanya Chatterjee 1 Olivier Commowick 1 Onur Afacan 2 Simon K Warfield 2 Christian Barillot 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : The brain microstructure, especially myelinated axons and free fluids, may provide useful insight into brain neurodegen-erative diseases such as multiple sclerosis (MS). These may be distinguished based on their transverse relaxation times which can be measured using T 2 relaxometry MRI. However, due to physical limitations on achievable resolution, each voxel contains a combination of these tissues, rendering the estimation complex. We present a novel multi-compartment T 2 (MCT2) estimation based on variable projection, applicable to any MCT2 microstructure model. We derive this estimation for a three-gamma distribution model. We validate our framework on synthetic data and illustrate its potential on healthy volunteer and MS patient data.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download

Contributor : Sudhanya Chatterjee Connect in order to contact the contributor
Submitted on : Tuesday, March 27, 2018 - 4:45:53 PM
Last modification on : Friday, April 8, 2022 - 4:04:03 PM
Long-term archiving on: : Thursday, September 13, 2018 - 10:08:19 AM


Files produced by the author(s)



Sudhanya Chatterjee, Olivier Commowick, Onur Afacan, Simon K Warfield, Christian Barillot. Multi-Compartment Model of Brain Tissues from T2 Relaxometry MRI Using Gamma Distribution. ISBI 2018 - IEEE International Symposium on Biomedical Imaging, Apr 2018, Washington DC, United States. pp.141-144, ⟨10.1109/ISBI.2018.8363541⟩. ⟨hal-01744852⟩



Record views


Files downloads