Comprehensive Maximum Likelihood Estimation of Diffusion Compartment Models Towards Reliable Mapping of Brain Microstructure

Aymeric Stamm 1, 2, * Olivier Commowick 3 Simon Warfield 2 Simone Vantini 1
* Corresponding author
1 MOX - Modeling and Scientific Computing [Milano]
2 CRL - Computational Radiology Laboratory [Boston]
3 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Diffusion MRI is a key in-vivo non invasive imaging capability that can probe the microstructure of the brain. However, its limited resolution requires complex voxelwise generative models of the diffusion. Diffusion Compartment (DC) models divide the voxel into smaller compartments in which diffusion is homogeneous. We present a comprehensive framework for maximum likelihood estimation (MLE) of such models that jointly features ML estimators of (i) the baseline MR signal, (ii) the noise variance, (iii) compartment proportions, and (iv) diffusion-related parameters. ML estimators are key to providing reliable mapping of brain microstructure as they are asymptotically unbiased and of minimal variance. We compare our algorithm (which efficiently exploits analytical properties of MLE) to alternative implementations and a state-of-the-art strategy. Simulation results show that our approach offers the best reduction in computational burden while guaranteeing convergence of numerical estimators to the MLE. In-vivo results also reveal remarkably reliable microstructure mapping in areas as complex as the centrum semi-ovale. Our ML framework accommodates any DC model and is available freely for multi-tensor models as part of the ANIMA software.
Document type :
Conference papers
Complete list of metadatas

Cited literature [60 references]  Display  Hide  Download
https://www.hal.inserm.fr/inserm-01349556
Contributor : Olivier Commowick <>
Submitted on : Wednesday, November 9, 2016 - 3:33:07 PM
Last modification on : Monday, March 4, 2019 - 2:07:42 PM
Long-term archiving on : Wednesday, March 15, 2017 - 12:14:46 AM

File

MICCAI2016.pdf
Files produced by the author(s)

Identifiers

Collections

INSTITUT-TELECOM | UNIV-RENNES1 | INRIA | IRISA | UR1-UFR-ISTIC | CENTRALESUPELEC | UNIV-RENNES | IRISA-D5

Citation

Aymeric Stamm, Olivier Commowick, Simon Warfield, Simone Vantini. Comprehensive Maximum Likelihood Estimation of Diffusion Compartment Models Towards Reliable Mapping of Brain Microstructure. 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct 2016, Athens, Greece. pp.622 - 630, ⟨10.1007/978-3-319-46726-9_72⟩. ⟨inserm-01349556⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

376

Files downloads

353

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD