Development and multiparametric monitoring of a microfluidic chip of the blood-brain barrier model
The blood-brain barrier (BBB) protects the brain by controlling exchanges between the blood and nervous tissue. However, current models struggle to accurately reproduce its complexity. This thesis aims at developing and evaluating a microfluidic chip of BBB model incorporating a real-time monitoring system that combines simultaneous optical and electrical measurements. The device will enable the study of permeability, transendothelial resistance and cellular response to various pharmacological or toxic stimuli. By combining microtechnologies, cell co-cultures and integrated sensors, this model of biological avatar will offer a more physiological and dynamic approach than conventional in vitro systems to improve understanding of the diffusion/permeation phenomena of therapeutic molecules. This project will contribute to the development of predictive tools for neuropharmacology, toxicology and research into neurodegenerative diseases.
Development and validation of surface haptics machine learning algorithms for touch and dexterity assessment in neurodevelopmental disorders
The aim of this PhD thesis is to develop new clinical assessment methods using surface haptics technologies, developed at CEA List, and machine learning algorithms for testing and monitoring tactile-motor integration. In particular, the thesis will investigate and validate the development of a multimodal analytics pipeline that converts surface haptics signals and dexterity exercises inputs (i.e. tactile stimulation events, finger kinematics, contact forces, and millisecond timing) into reliable, interpretable biomarkers of tactile perception and sensorimotor coupling, and then classify normative versus atypical integration patterns with clinical fidelity for assessment.
Expected results: a novel technology and models for the rapid and feasible measurement of tactile-motor deficits in clinical setting, with an initial validation in different neurodevelopmental disorders (i.e. first-episode psychosis, autism spectrum disorder, and dyspraxia). The methods developed and data collected will provide:
(1) an open, versioned feature library for tactile–motor assessment;
(2) classifiers with predefined operating points (sensitivity/specificity);
(3) and an on-device/edge-ready pipeline, i.e. able to run locally on a typical tablet hardware whilst meeting constraints on latency, computing, and data privacy. Success will be measured by reproducibility of features, clinically meaningful effect sizes, and interpretable decision logic that maps back to known neurophysiology rather than artefacts.
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.
Optimization of gamma radiation detectors for medical imaging. Time-of-flight positron emission tomography
Introduction
Innovative functional imaging technologies are contributing to the CEA's ‘Medicine for the Future’ priority. Positron emission tomography (PET) is a nuclear medical imaging technique widely used in oncology and neurobiology. The decay of the radioactive tracer emits positrons, which annihilate into two photons of 511 keV. These photons are detected in coincidence and used to reconstruct the distribution of tracer activity in the patient's body.
We're proposing you to contribute to the development of an ambitious, patented technology: ClearMind. The first prototype is in our laboratories. This gamma photon detector uses a monolithic scintillating crystal of high density and atomic number, in which Cherenkov and scintillation photons are produced. These optical photons are converted into electrons by a photoelectric layer and multiplied in a MicroChannel plate. The induced electrical signals are amplified by gigahertz amplifiers and digitized by SAMPIC fast acquisition modules. The opposite side of the crystal will be fitted with a matrix of silicon photomultiplier (SiPM).
Today we have our first prototype, and we are preparing two more.
The proposed work
You will work in an advanced instrumentation laboratory in a particle physics environment .
The first step will be to optimize the "components" of ClearMind detectors, in order to achieve nominal performance. We'll be working on scintillating crystals, optical interfaces, photoelectric layers and associated fast photodetectors (MCP-PMT and SiPM), and readout electronics.
We will then characterize the performance of the prototype detectors on our measurement benches, which are under continuous development. The data acquired will be interpreted using in-house analysis software written in C++ and/or Python.
Finally, we will compare the physical behavior of our detectors to Monté-Carlo simulation software (Geant4/Gate).
A particular effort will be devoted to the development of ultra-fast scintillating crystals in the context of a European collaboration.
Supervision
The successful candidate will work under the joint supervision of Dominique Yvon and Viatcheslav Sharyy (DRF/IRFU & BIOMAPS). The CaLIPSO group at IRFU & BIOMAPS specializes in the development and characterization of innovative PET detectors, including detailed detector simulation. As part of the project, we are working closely with IJCLabs in Orsay, which is developing our readout and acquisition electronics, CEA/DM2S, which is working in particular on trusted AI algorithms, CPPM in Marseille, which is evaluating our detectors under PET imaging acquisition conditions, and UMR BIOMAPS (CEA/SHFJ), working on image calculation algorithms.
Requirements
Knowledge of the physics of particle-matter interaction, radioactivity and the principles of particle detectors is essential. A strong interest in instrumentation and laboratory work is recommended. Basic programming skills, e.g. C++, Gate/Geant4 physics simulation software, are important.
Skills acquired
Good knowledge of state-of-the-art particle detector and positron emission tomography technologies. Simulation principles and techniques for particle-matter interaction and detection systems. Analysis of complex data.
Contact
Dominique Yvon, dominique.yvon@cea.fr
Viatcheslav Sharyy, viatcheslav.sharyy@cea.fr