Main interests

My main interest is focused on developing methods and applications for biomedical signal and image processing/analysis and remote sensing. Developments involve machine/deep learning techniques, as well as  Bayesian, variational and hybrid methods.



  1. Image reconstruction and restoration (MRI, pMRI, MRSI)
  2. Tumor relapse prediction (MRSI, MRI, PET)
  3. Brain activity analysis (fMRI, detection-estimation)
  4. EEG source localization
  5. Drowsiness detection (EEG, ECG)
  6. Anomaly detection (EEG, ECG, MRSI, PET)
  1. Change detection (multi-temporal and hyperspectral data)
  2. Hyperspectral unmixing
  3. Anomaly detection
        Funding : Ligue Nationale Contre le Cancer
        Funding : Fondation pour la recherche sur le cancer (ARC)

  • Bayesian Optimisation for Quantum Machine Learning 
        Funding : Toulouse INP
  • Interoperability of Biomedical Connected Objects
        Funding : CIMI

  • Assessment of the risks of disorders in physiological signals
        Funding : Toulouse Tech Transfer (maturation)
  • Assessment of the risks of disorders in physiological signals
        Funding : Toulouse Tech Transfer (pré-maturation)

  • Analysis of multi-temporal images by joint detection-estimation: detection of changes in multi-temporal optical images
        Funding : CNES
        Partners : Tésa, IRIT, CNES

  • OPTIMISME : design of a new generation of parallel algorithms exploiting recent advances in stochastic optimization for processing large amounts of data
        Funding : CNRS
        Partners : LIGM, LJLL, IRIT, Laboratoire de Physique de l’ENS de Lyon, INT, I2M

  • Skin Lentigo characterization through reflectance confocal image analysis
        Funding : Pierre Fabre Laboratory
        Partners : CERPER, Pierre Fabre Toulouse
  • Telemetry analysis for diagnosis: analysis of satellite telemetry data for anomaly detection (2014-2015)
        Funding : CNES
        Partners : Tésa, IRIT, CNES, Université de Nice Sophia Antipolis (Lab. J.L. Lagrange )

  • DynBrain :Reconstruction of Brain Dynamic EEG images
        Funding : STIC -AmSUd Program
        Partners : IRIT-INPT, Univ. Sanata Catarina - Brazil, ITBA- Buenos Aires

Some collaborations...



Topic: Parallel Magnetic Resonance Imaging reconstruction problems using wavelet representations
Supervisors: Jean-Christophe PESQUET and Philippe CIUCIU
Team: Signal and Communications, IGM LabInfo, UMR8049, University of Marne la Vallee.
Research context:
To reduce scanning time or improve spatio-temporal resolution in some MRI applications, parallel MRI acquisition techniques with multiple coils have emerged since the early 90's as powerful methods. In these techniques, MRI images have to be reconstructed from acquired undersampled "k-space" data. To this end, several reconstruction techniques have been proposed such as the widely-used SENSitivity Encoding (SENSE) method. However, the reconstructed images generally present artifacts due to the noise corrupting the observed data and coil sensitivity profile estimation errors. In this work, we present novel SENSE-based reconstruction methods which proceed with regularization in the complex wavelet domain so as to promote the sparsity of the solution. These methods achieve accurate image reconstruction under degraded experimental conditions, in which neither the SENSE method nor standard regularized methods (e.g. Tikhonov) give convincing results. The proposed approaches relies on fast parallel optimization algorithms dealing with convex but non-differentiable criteria involving suitable sparsity promoting priors. Moreover, in contrast with most of the available reconstruction methods which proceed by a slice by slice reconstruction, one of the proposed methods allows 4D (3D + time) reconstruction exploiting spatial and temporal correlations. The hyperparameter estimation problem inherent to the regularization process has also been addressed from a Bayesian viewpoint by using MCMC techniques. Experiments on real anatomical and functional data show that the proposed methods allow us to reduce reconstruction artifacts and improve the statistical sensitivity/specificity in functional MRI.