Mois : February 2019
PhD Seminars Special Edition – GDPR and research
Speaker Anne Combe – Déléguée à la Protection des Données Title GDPR and research Abstract This talk will address the following questions What is the General Data Protection Regulation? What changes does GDPR bring? What are the obligations of each of us? What is the impact on researchers? Contents:…
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Calendar
mai 2021
lundi
mardi
mercredi
jeudi
vendredi
samedi
dimanche
avril 26, 2021
2:00 pm: PhD Seminars IX
2:00 pm: PhD Seminars IX
avril 26, 2021
–
Talk 1
Speaker
Sara Sedlar (Athena)
Title
A Fourier domain spherical convolutional neural network for brain tissue microstructure imaging via diffusion MRI
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive and in-vivo imaging technique tailored for tissue examination at a microscopic scale. Consequently, it is essential in the analysis of tissue microstructures of the central nervous system. To explain the measured signals, a number of biophysically inspired multi-compartment (MC) models have been proposed. They represent dMRI data as a linear combination of signals coming from different tissue compartments such as intra- and extra-axonal spaces, gray matter, cerebrospinal fluid, tumorous cells, etc. Multiple studies have shown that the parameters associated with some of these models have potential in the evaluation of several neurological diseases and in the characterization of early age brain development. However, estimation of these parameters via standard non-linear optimizers which include Levenberg-Marquardt and Gauss-Newton algorithms, often require a high number of sampling points and/or are computationally demanding, which limits their clinical application. Since in our work, we are considering dMRI signals acquired on spheres, to address the problem of microstructure parameter estimation, we propose a spherical CNN model with fully spectral domain convolutional and non-linear layers and with rotation invariant power spectrum features. In addition, the model takes into account the real nature of dMRI signals, uniform random distribution of sampling points and important noise which affects these signals. The proposed model is evaluated quantitatively and qualitatively on the problem of Neurite Orientation Dispersion and Density Imaging (NODDI) and Spherical Mean Technique (SMT) parameter estimation. The model is positively evaluated on the real data from Human Connectome Project (HCP) database and on the synthetic data generated by dmipy toolbox.
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PhD Seminars Special Edition – GDPR and research
Speaker Anne Combe – Déléguée à la Protection des Données Title GDPR and research Abstract This talk will address the following questions What is the General Data Protection Regulation? What changes does GDPR bring? What are the obligations of each of us? What is the impact on researchers? Contents:…
-
Calendar
mai 2021 lundi mardi mercredi jeudi vendredi samedi dimanche avril 26, 2021 2:00 pm: PhD Seminars IX
2:00 pm: PhD Seminars IXavril 26, 2021 –
Talk 1
Speaker
Sara Sedlar (Athena)
Title
A Fourier domain spherical convolutional neural network for brain tissue microstructure imaging via diffusion MRI
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive and in-vivo imaging technique tailored for tissue examination at a microscopic scale. Consequently, it is essential in the analysis of tissue microstructures of the central nervous system. To explain the measured signals, a number of biophysically inspired multi-compartment (MC) models have been proposed. They represent dMRI data as a linear combination of signals coming from different tissue compartments such as intra- and extra-axonal spaces, gray matter, cerebrospinal fluid, tumorous cells, etc. Multiple studies have shown that the parameters associated with some of these models have potential in the evaluation of several neurological diseases and in the characterization of early age brain development. However, estimation of these parameters via standard non-linear optimizers which include Levenberg-Marquardt and Gauss-Newton algorithms, often require a high number of sampling points and/or are computationally demanding, which limits their clinical application. Since in our work, we are considering dMRI signals acquired on spheres, to address the problem of microstructure parameter estimation, we propose a spherical CNN model with fully spectral domain convolutional and non-linear layers and with rotation invariant power spectrum features. In addition, the model takes into account the real nature of dMRI signals, uniform random distribution of sampling points and important noise which affects these signals. The proposed model is evaluated quantitatively and qualitatively on the problem of Neurite Orientation Dispersion and Density Imaging (NODDI) and Spherical Mean Technique (SMT) parameter estimation. The model is positively evaluated on the real data from Human Connectome Project (HCP) database and on the synthetic data generated by dmipy toolbox.
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