Cortex parcellation via diffusion data as prior knowledge for the MEG inverse problem
Résumé
In this paper, we present a new approach to the recovery of dipole magnitudes in a distributed source model for magnetoencephalographic (MEG) imaging. This method consists in introducing prior knowledge regarding the anatomical connectivity in the brain to this ill-posed inverse problem. Thus, we perform cortex parcellation via structural information coming from diffusion MRI (dMRI), the only non-invasive modality allowing to have access to the structure of the WM tissues. Then, we constrain, in the MEG inverse problem, sources in the same diffusion parcel to have close magnitude values. Results of our method on MEG simulations are presented and favorably compared with classical source reconstruction methods.
Mots clés
biodiffusion
biological tissues
biomedical MRI
magnetoencephalography
medical image processing
MEG inverse problem
MEG simulation
WM tissue structure
anatomical connectivity
brain
classical source reconstruction method
cortex parcellation
diffusion MRI
diffusion data
diffusion parcel
dipole magnitude recovery
distributed source model
ill-posed inverse problem
magnetoencephalographic imaging
magnitude value
noninvasive modality
prior knowledge
structural information
Equations
Image reconstruction
Inverse problems
Magnetic resonance imaging
Noise
Vectors
dMRI