000200934 001__ 200934
000200934 005__ 20190508194205.0
000200934 0247_ $$2doi$$a10.1109/TMI.2014.2352414
000200934 022__ $$a0278-0062
000200934 02470 $$2ISI$$a000346975900023
000200934 037__ $$aARTICLE
000200934 245__ $$aCOMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
000200934 269__ $$a2015
000200934 260__ $$bInstitute of Electrical and Electronics Engineers$$c2015$$aPiscataway
000200934 300__ $$a12
000200934 336__ $$aJournal Articles
000200934 520__ $$aTractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion MRI data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this article, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e. the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically-plausible assessment of the structural connectivity of the brain.
000200934 6531_ $$aDiffusion MRI
000200934 6531_ $$aGlobal tractography
000200934 6531_ $$aTissue microstructure
000200934 6531_ $$aConvex optimization
000200934 6531_ $$aLTS5
000200934 6531_ $$aCIBM-SPC
000200934 700__ $$0242941$$g193105$$aDaducci, Alessandro
000200934 700__ $$aDal Palú, Alessandro
000200934 700__ $$0245880$$g173135$$aLemkaddem, Alia
000200934 700__ $$aThiran, Jean-Philippe$$g115534$$0240323
000200934 773__ $$j34$$tIEEE Transactions on Medical Imaging$$k1$$q246-257
000200934 8564_ $$uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6884830$$zURL
000200934 8564_ $$uhttps://github.com/daducci/COMMIT$$zURL
000200934 8564_ $$uhttps://infoscience.epfl.ch/record/200934/files/main.pdf$$zn/a$$s3638330$$yn/a
000200934 909C0 $$xU10954$$0252394$$pLTS5
000200934 909CO $$ooai:infoscience.tind.io:200934$$qGLOBAL_SET$$pSTI$$particle
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x193105
000200934 917Z8 $$x173135
000200934 937__ $$aEPFL-ARTICLE-200934
000200934 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000200934 980__ $$aARTICLE