Non-invasively exploring the cerebellum microstructure with magnetic resonance
To better diagnose and monitor brain diseases, we need “non-invasive biopsies” to access the tissue cell-type composition and state without opening the skull. Magnetic resonance imaging (MRI) research efforts attempt to tackle the challenge but often lack cellular specificity because of the ubiquitous nature of water. Diffusion-weighted magnetic resonance imaging (dMRS) measures diffusion of intracellular and partly cell specific molecules in a region of interest, and forms a solid basis for resolving cell-types non-invasively. Among challenges, resolving signal contributions from the different cerebellar neurons could help monitor and understand neurodevelopmental and ataxic disorders. The cerebellum is a brain region representing 10% of the brain volume but containing more than half of the brain neurons, with the very large and complex Purkinje cells and the very small and round granule cells, both having very different functions and metabolism. The PhD project aims to disentangle these cells with complementary strategies: a classical dMRS approach and a quantum dMRS approach confronted to the state-of-the-art microstructure MRI methods.
Advanced methods of blockwise diffusion imaging for studying fetal cerebral development at the mesoscopic scale
The second half of pregnancy is an extremely rich period in terms of brain development, during which key processes such as neurogenesis, neuronal migration, and axonal growth take place; transient structures form and disappear, while brain volume increases more than tenfold. A blockwise ex-vivo imaging technique recently developed in NeuroSpin allows us to take a new look on developing brain tissues, leveraging ultra-high-field MRI at 11.7 teslas to acquire unprecedented whole-brain images at mesoscopic resolution (100 to 200 µm 3D isotropic) . The acquired data is highly multiparametric, including quantitative T1, T2, and T2* mapping, as well as high angular resolution, multi-shell diffusion-weighted imaging (b = 1500, 4500, 8000 s/mm² with 25, 60, and 90 directions respectively) at 200 µm isotropic resolution.
In order to reach such a high level of detail, a small-bore scanner is used (5 cm usable diameter) over extended scanning times (150 hours per field of view). Brains older than about 20 gestational weeks are too large, and are sectioned into blocks whose size is compatible with the scanner. The resulting blockwise images are registered using a dedicated semi-automatic protocol, and fused to reconstruct a set of whole-brain images. While this protocol has allowed us to obtain good-quality images on several fetal brain specimens (3 published, 3 other brains in progress as of the end of 2025), the diffusion imaging data remains to be fully analyzed: indeed, the blockwise nature of the acquisitions poses unique challenges, notably due to the discontinuity at the boundary between blocks, but also to non-linear image deformations and non-linearity of the magnetic field gradients.
The PhD candidate will be hosted in the inDEV team (imaging neurodevelopmental phenotypes) in close collaboration (co-supervision) with the Ginkgo team, which has leading expertise in diffusion imaging methods and has pioneered the blockwise acquisition technique in an adult brain known as Chenonceau. The PhD work lies at the interface between imaging, algorithmics, and developmental neuroscience: it will include developing and benchmarking new methods for processing this blockwise diffusion MRI to obtain high-quality tractography and fit diffusion microstructural models. It will also include an experimental part, where the PhD candidate will take part in the acquisition and reconstruction of new brains, both typical specimens and pathological ones with agenesis of the corpus callosum. Finally, the candidate will explore neuroscientific outcomes of this unprecedented dataset, which has exceptional potential to describe processes such as the development of subcortical pathways and associative white matter fibre tracts, and to become the first atlas of the developing fetal brain with fibre architecture at the mesoscopic scale.
Super-resolution of brain MR images: from research to the clinic through machine learning approaches.
Magnetic Resonance Imaging (MRI) has become a reference modality for diagnosing and monitoring neurological disorders. However, acquiring high-resolution (HR) brain images remains challenging in clinical practice due to limited scan time, patient comfort constraints, and image degradation caused by patient motion. The increased signal enabled by higher magnetic field strengths can be invested to achieve higher spatial resolution within the same acquisition time. This project aims at taking advantage of the unprecedented spatial resolution achievable with the 11.7T Iseult MRI scanner, currently the most powerful MR scanner in the world, to train a machine learning-based super-resolution (SR) model that enhances the spatial resolution of 3T MRI images acquired in clinical practice. Current SR approaches are typically trained on public datasets, using pairs of high- and low-resolution images, with the low-resolution data synthetically generated from the high-resolution images. In this project we will use a real dataset consisting of 3T and 11.7T images acquired from the same cohort, ensuring higher anatomical fidelity and enabling a rigorous assessment of hallucination risks, i.e. of generating anatomically incorrect details that could be misinterpreted by the radiologists. The project will involve the following steps: improving the quality of 11.7T images (through motion correction and artifact reduction), acquiring pairs of images at 3T and 11.7T, developing and validating SR models, and finally assessing their generalizability on public datasets. This work supports the integration of reliable SR methods into clinical practice, allowing conventional MRI scanners to benefit indirectly from Iseult's unique capabilities.
Fast labeling of antibodies with fluorine-18: toward simplified and efficient processes for medical imaging
The development of innovative tools in medical imaging represents a major lever for the early and accurate diagnosis of cancer. The combination of PET imaging and antibodies as targeting vectors stands among the most specific approaches for tumor detection. This potential is reflected by the widespread use of these tools in preclinical research, but their translation into routine clinical practice remains challenging, mainly due to the non-negligible dosimetry of the positron-emitting isotopes currently used (8?Zr, 64Cu).
This PhD project aims to develop an antibody-labeling technology using fluorine-18, the reference isotope for clinical PET imaging. Labeling antibodies whose biodistribution time is compatible with this isotope represents a significant technical challenge and requires a non-negligible reaction time compared to the short half-life of fluorine-18. The objective of this project is to simplify and accelerate this process through the development of a chemical tag that would enable the direct labeling of antibodies with fluorine-18 produced by the cyclotron, analogously to the longer-lived radiometals classically used. Several challenges will be addressed in developing this tag, including the optimization of its fluorine-18 radiolabeling and its bioconjugation to antibodies, with the ultimate goal of providing a simple, fast, and clinically translatable tool.