Abib Alimi
My research interests lie in Healthcare. I am a Postdoctoral Research Associate in the Computer Science Department, at
Princeton University
where I work with Professor Adji Bousso Dieng at the Vertaix lab.
I work on developing methods at the intersection of Artificial Intelligence and Materials Science to tackle problems related to medical imaging.
I completed my Ph.D in Automatic, Signal and Image Processing from Inria,
University of Côte d'Azur, France, where I was jointly advised by Rachid Deriche
and Samuel Deslauriers-Gauthier.
My Ph.D research delt with the reconstruction of diffusion Magnetic Resonance Imaging (MRI) signal of the brain,
and its complementary analysis with 3D-Polarized Light Imaging (3D-PLI).
I performed multiscale and multimodal image analysis between diffusion MRI and 3D-PLI.
I received the ISMRM Magna Cum Laude Award.
CV  / 
Github  / 
LinkedIn  / 
Email: aa0539 at princeton dot edu
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Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging
Abib Alimi,
Samuel Deslauriers-Gauthier,
Felix Matuschke,
Andreas Müller,
Sascha Münzing,
Markus Axer,
Rachid Deriche
Medical Image Analysis Journal, 2020
bibtex
An analytical and fast method is introduced to elegantly compute the fiber orientation distribution function (fiber ODF)
from high-resolution vector data provided by 3D-PLI of the human brain.
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A spherical convolutional neural network for white matter structure imaging via dMRI
Sara Sedlar,
Abib Alimi,
Théo Papadopoulo,
Rachid Deriche,
Samuel Deslauriers-Gauthier
MICCAI, 2021
bibtex
The rotational invariance of diffusion MRI signal is considered
in a new spherical CNN model with fully spectral domain convolutional and non-linear layers
to estimate white matter microstructure parameters.
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Dmipy, a Diffusion Microstructure Imaging toolbox in Python to improve research reproducibility
Abib Alimi,
Rutger Fick,
Demian Wassermann,
Rachid Deriche
CDMRI, 2018
bibtex
We present Diffusion Microstructure Imaging in Python (Dmipy), a diffusion MRI toolbox which allows accessing any multi-compartment-based model
and robustly estimates these important features from single-shell, multi-shell, and multi-diffusion time, and multi-TE data.
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An analytical fiber ODF reconstruction in 3D-Polarized Light Imaging
Abib Alimi,
Yves Usson,
Pierre-Simon Jouk,
Gabrielle Michalowicz,
Rachid Deriche
IEEE-ISBI, 2018   (Oral Presentation)
bibtex
We propose an analytical method to compute the fiber orientation distribution function (fiber ODF)
from high-resolution vector data provided by 3D-PLI.
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Solving the Inclination Sign Ambiguity in Three Dimensional Polarized Light Imaging with a PDE-Based Method
Abib Alimi,
Marco Pizzolato,
Rutger H.J. Fick,
Rachid Deriche
IEEE-ISBI, 2017   (Oral Presentation)
bibtex
A partial differential equations (PDE)-based mthod is proposed to reduce the noise from tilted measurements and
disentangle the sign ambiguity of the inclination angle for a more accurate reconstruction and interpretation of the 3D-PLI fiber orientation.
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Abstracts and Presentations
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Quantitative assessment of multi-scale tractography: bridging the resolution gap with 3D-PLI
Abib Alimi,
Matteo Frigo,
Samuel Deslauriers-Gauthier,
Rachid Deriche
ISMRM, 2020
bibtex
We show how tractograms obtained at different spatial scales from 3D-PLI human brain datasets can be inspected
using homology theory in order to perform a quantitative comparison between them.
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Towards validation of diffusion MRI tractography: bridging the resolution gap with 3D-Polarized Light Imaging
Abib Alimi,
Samuel Deslauriers-Gauthier,
Rachid Deriche
ISMRM, 2019   (Oral Presentation, Magna Cum Laude Award)
bibtex
From the analytical fiber ODFs reconstructed from 3D-PLI datasets,
we compute probabilistic brain fiber tractography at different spatial resolution scales
to close the resolution gap between 3D-PLI and diffusion MRI.
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Spherical convolutional neural network for diffusion MRI analysis
Sara Sedlar,
Abib Alimi,
Théo Papadopoulo,
Rachid Deriche,
Samuel Deslauriers-Gauthier
Sophia Summit, 2019   (Oral Presentation)
bibtex
We embed the specific diffusion MRI signal features (spherical representation, antipodal symmetry)
to build new spherical convolutional neural netwoks.
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Towards the assessment of myelination using time-dependent diffusion MRI indices
Abib Alimi,
Alexandra Petiet,
Mathieu Santin,
Anne-Charlotte Philippe,
Stephane Lehericy,
Rachid Deriche,
Demian Wassermann
ISMRM, 2018   (E-poster)
bibtex
This work explores, for the very first time to our knowledge, the potential of the time-dependent
diffusion microstructure indices (qτ-indices) as accurate biomarkers in order to understand and
efficiently treat myelin-related pathologies, in vivo and non invasively.
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Teaching
I have been a teaching assistant at University Côte d'Azur in:
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Unified Modeling Language for Software system design, Spring 2018
Unified Modeling Language for Software system design, Fall 2017
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