Year: 2021
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Calendar
May 2021
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
April 26, 2021
2:00 pm: PhD Seminars IX
2:00 pm: PhD Seminars IX
April 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.
April 27, 2021
April 28, 2021
April 29, 2021
April 30, 2021
May 1, 2021
May 2, 2021
May 3, 2021
May 4, 2021
May 5, 2021
May 6, 2021
May 7, 2021
May 8, 2021
May 9, 2021
May 10, 2021
May 11, 2021
May 12, 2021
May 13, 2021
May 14, 2021
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May 19, 2021
May 20, 2021
May 21, 2021
May 22, 2021
May 23, 2021
May 24, 2021
May 25, 2021
May 26, 2021
May 27, 2021
May 28, 2021
May 29, 2021
May 30, 2021
May 31, 2021
June 1, 2021
June 2, 2021
June 3, 2021
June 4, 2021
June 5, 2021
June 6, 2021
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Calendar
May 2021 Monday Tuesday Wednesday Thursday Friday Saturday Sunday April 26, 2021 2:00 pm: PhD Seminars IX
2:00 pm: PhD Seminars IXApril 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.
April 27, 2021 April 28, 2021 April 29, 2021 April 30, 2021 May 1, 2021 May 2, 2021 May 3, 2021 May 4, 2021 May 5, 2021 May 6, 2021 May 7, 2021 May 8, 2021 May 9, 2021 May 10, 2021 May 11, 2021 May 12, 2021 May 13, 2021 May 14, 2021 May 15, 2021 May 16, 2021 May 17, 2021 May 18, 2021 May 19, 2021 May 20, 2021 May 21, 2021 May 22, 2021 May 23, 2021 May 24, 2021 May 25, 2021 May 26, 2021 May 27, 2021 May 28, 2021 May 29, 2021 May 30, 2021 May 31, 2021 June 1, 2021 June 2, 2021 June 3, 2021 June 4, 2021 June 5, 2021 June 6, 2021
