Novel 4-D Algorithm for Functional MRI Image Regularization using Partial Differential Equations

Isa Costantini (1, 2) , Samuel Deslauriers-Gauthier (1, 2) , Rachid Deriche (2, 1)

1 COMUE UCA - COMUE Université Côte d'Azur (2015-2019)
2 ATHENA - Computational Imaging of the Central Nervous System

State-of-the-art techniques for denoising functional MRI (fMRI) images consider the problems of spatial and temporal regularization as decoupled tasks. In this work we propose a partial differential equations (PDEs)-based algorithm that acts directly on the 4-D fMRI image. Our approach is based on the idea that large image variations should be preserved as they occur during brain activation, but small variations should be smoothed to remove noise. Starting from this principle, by means of PDEs we were able to smooth the fMRI image with an anisotropic regularization, thus recovering the location of the brain activations in space and their timing and duration.

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

Isa Costantini, Samuel Deslauriers-Gauthier, Rachid Deriche. Novel 4-D Algorithm for Functional MRI Image Regularization using Partial Differential Equations. ISMRM 2019 - 27th Annual Meeting of International Society for Magnetic Resonance in Medicine, May 2019, Montréal, Canada. ⟨hal-02074345⟩