Mathematical methods for diffusion MRI processing.

C. Lenglet, J. S. Campbell, M. Descoteaux, G. Haro, P. Savadjiev, D. Wassermann, A. Anwander, R. Deriche, G. B. Pike, G. Sapiro, K. Siddiqi, P. M. Thompson

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


In this article, we review recent mathematical models and computational methods for the processing of diffusion Magnetic Resonance Images, including state-of-the-art reconstruction of diffusion models, cerebral white matter connectivity analysis, and segmentation techniques. We focus on Diffusion Tensor Images (DTI) and Q-Ball Images (QBI).

Original languageEnglish (US)
Pages (from-to)S111-122
Issue number1 Suppl
StatePublished - Mar 2009

Bibliographical note

Funding Information:
This work was partially supported by NIH (P41 RR008079, P30 NS057091, R01 EB007813, CON000000004051-3014, RO1 HD050735, P41 RR013642), ONR, NGA, NSF, DARPA, ARO, INRIA/NSF (0404617) under the US-France Cooperative Research Program, INRIA-Odyssée/MPI-HCBS PAI Procope, INRIA (DMRI ARC grant), INRIA/FQRNT/FFCR/Québec, NSERC Canada, the INRIA-Odyssée/EADS Foundation grant and the Spanish Ministerio de Ciencia e Innovación, under program Juan de la Cierva and project TEC2007-66858/TCM.


Dive into the research topics of 'Mathematical methods for diffusion MRI processing.'. Together they form a unique fingerprint.

Cite this