### Context
Magnetic resonance imaging (MRI) and in particular diffusion MRI (dMRI) provide detailed information about the macroscopic organisation of brain white matter (WM) fiber bundles (see Figure), with a method called fiber tractography. Complementary to the geometry of fibers, dMRI is also sensitive to the microscopic tissue structure and its alteration with pathology. The joint analysis of white matter fascicles and their associated microstructure organisation requires the development of specific mathematical representations.
### Main objectives of the thesis
The main objective of this thesis will be the development of mathematical models of microstructure-augmented fascicle (MAF), which convey both the macro-structural information provided by tractography and the microstructural information provided by the diffusion models along the WM fascicles. In the context of the PASTRAMI (**Pa**tient-specific **s**tatistics for micros**tr**ucture-**a**ugmented connecto**mi**cs) collaborative project (funded by the PRC program, agence nationale de la recherche, 2023-2028), these representations will be used to derive patient-specific biomarkers of functional recovery in patients suffering from severe traumatic brain injury.
### Methodology
We will develop upon a shape analysis frameworks such as the LDDMM (Large Diffeomorphic Metric Mapping) framework that relies on Riemannian geometry and is well adapted to the study of anatomical structures, to construct the models for the representation of fiber bundles (which can be defined as 1-dimensional curves or 2-dimentional surfaces in R^3) and their associated microstructure. In continuation, we will also analyse the brain connectome, which represents the network of connected gray matter regions in the brain. We will build upon methods developed for the analysis of graphs with complex data.
### Main activities
- Write scientific publications
- Present the works and results in international conference
### Skills
We look for candidates strongly motivated by challenging research topics in neuroimaging. The applicant should present a good background in applied mathematics. Basic knowledge in image processing would be a plus. Good knowledge of computer science aspects is also mandatory, especially in Python and C++.
###Location:
The recruited person will work at Inria/IRISA, UMR CNRS 6074, among the Empenn U1228 team. The candidate will also benefit of a multidisciplinary environment, as within the context this thesis. Inria is a French laboratory for research and innovation in digital science and technology and offers a PhD funding. Successful candidates will also benefits of annual paid holidays and social insurance.
Contact: julie.coloigner@irisa.fr