Doctorant F/H Anatomical and microstructure informed tractography for connectivity evaluation

Doctorant F/H Anatomical and microstructure informed tractography for connectivity evaluation The offer description be low is in French Level of qualifications required :Graduate degree or equivalentFonction :PhD PositionAbout the research centre or Inria departmentContextDiffusion MRI (dMRI) quantifies the diffusion of water molecules (constrained by their environment), enabling us to infer a number of microstructure parameters, such as the arrangement of nerve fibers, the different tissues making them up and their properties (axon diameter, proportion of neuronal cell bodies, etc.). Additionally, dMRI combined with white matter tractography techniques is a highly promising method for assessing the trajectories of nerve fibers in the brain. More specifically, tractography (illustrated in figure 1) utilizes the directionality of diffusion of water molecules in brain tissue to estimate neuronal fiber orientation (D. Jones, 2010). This process is known as fiber tracking or fiber tractography, and the resulting collection of white matter pathways is referred to as tractograms (Mori and Van Zijl, 2002). These approaches have the remarkable capability to delineate white matter fiber pathways, offering unprecedented insights into the structural connections within the human brain. They hold enormous potential for studying brain anatomy, development, and function (Jeurissen et al., 2019a). Furthermore, tractography has demonstrated its substantial worth in the field of neurosurgery, playing a pivotal role in surgical planning, particularly in the preservation of critical white matter pathways during brain resections (Mancini et al., 2019).Despite advancements in dMRI acquisition and tracking methods, white matter fiber tractography continues to grapple with certain limitations. Recent studies reported the existence of a significant number of connections that remain undetected by tractography, resulting in false negatives (D. B. Aydogan et al., 2018). This issue poses a critical challenge, particularly in applications like surgical planning. Furthermore, the outcomes of other studies indicate that state-of-the-art tractography algorithms produce substantial numbers of false positives as well (K. Maier-Hein et al., 2017b). This drawback hampers the accurate exploration of network properties within the brain’s connectome (Zalesky et al., 2016).Despite this, such tractography approaches remain limited for a variety of reasons. Firstly, most of the studies use a simple diffusion model such as diffusion tensor imaging (DTI) , which cannot estimate fibers with different orientation in one voxel in complex areas. Moreover, the interpretation of changes in the measured diffusion tensor is complex and should be performed with care. Furthermore, the estimation of cerebral fibers (tractography (8), illustrated in figure 1) is not yet reliable. Studies have shown that the most advanced tractography algorithms tend to generate a large number of fiber bundles, resulting in a high false-positive rate. In this thesis, our aim was to propose innovative methods for improving fiber estimation.To overpass that, new approaches have been proposed that include anatomical a priori to guide the algorithms in complex regions. In the Empenn team, we recently developed methods for creating and combining anatomical a priori using Riemannian geometry, applicable to any orientation distribution function (ODF)-based tractography algorithms.AssignmentThis PhD will focus on two major subjects:- Improving the atlas creation using multi-atlasing in order to take into account the tractogram variability- Improving a priori estimation using microstructural features to guide tractography, proposed during the PhD thesis of Thomas Durantel, by taking into account the variability of track orientation and the TOD estimation.- Incorporation of anatomical a priori - fiber bundle atlas, microstructural information from relaxometry or diffusion imaging along known, manually delineated fibers - and data to help tractography and avoid false positives.Main activitiesThe proposed method will be based on the track orientation distribution (TOD) (Dhollander et al., 2014) from an atlas of segmented fiber bundles and incorporates it during the tracking process, using a Riemannian framework. The developed approach will be tested on a cohort of patients suffering from depression, with the aim of better estimating the microstructure and thus better understanding the neuronal modifications caused by this disease.SkillsWe 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++.Benefits packageRemuneration Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.Instruction to applyDefence Security :This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.Recruitment Policy :As part of its diversity policy, all Inria positions are accessible to people with disabilities.About InriaInria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact. #J-18808-Ljbffr