Reducing false positive connection in tractograms using joint structure-function filtering

Matteo Frigo (1, 2) , Guillermo Gallardo (1, 2) , Isa Costantini (1, 2) , Alessandro Daducci (3) , Demian Wassermann (4) , Rachid Deriche (1, 2) , Samuel Deslauriers-Gauthier (1, 2)

1 ATHENA - Computational Imaging of the Central Nervous System
2 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)
3 UNIVR | DI - Department of Computer Science [Verona]
4 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

Due to its ill-posed nature, tractography generates a significant number of false positive connections between brain regions [3]. To reduce the number of false positives, Daducci et al. [1] proposed the COMMIT framework, which has the goal of re-establishing the link between tractography and tissue microstructure. In this framework, the diffusion MRI signal is modeled as a linear combination of local models associated with streamlines where the weights are identified by solving a convex optimization problem. Streamlines with a weight of zero do not contribute to the diffusion MRI data and are assumed to be false positives. Removing these false positives yields a subset of streamlines supporting the anatomical data. However, COMMIT does not make use of the link between structure and function and thus weights all bundles equally. In this work, we propose a new strategy that enhances the COMMIT framework by injecting the functional information provided by functional MRI. The result is an enhanced tractogram filtering strategy that considers both functional and structural data.

Domaines: Traitement du signal et de l'image [eess.SP]; Neurosciences

Matteo Frigo, Guillermo Gallardo, Isa Costantini, Alessandro Daducci, Demian Wassermann, et al.. Reducing false positive connection in tractograms using joint structure-function filtering. OHBM 2018 - Organization for Human Brain Mapping, Jun 2018, Singapore, Singapore. pp.1-3. ⟨hal-01737434⟩