The selected candidate will work on integrating the images produced in a post-processing pipeline developed in Python. He/she will work on the:

-development of pre-processing tools for spectroscopy data (filtering, denoising, rephrasing) which remains a challenge to obtain the best possible signal quality (best signal to noise ratio), before the quantization step

-Integration of a quantification algorithm (LCModel) for multi-kernel MRS data to facilitate metabolic biomarkers search of Alzheimer's disease (AD) at 3 T and 7T.
-Integration of tools to prepare MRI data (registration, skull strip-ping, segmentation) to be used with both MRS and PET on the same platform

In order to make the results closer to the ground truth (simulated brain concentration or metabolic concentration defined with acquisition on phantom), It is necessary to validate each step of the pipeline in order to increase the robustness of the proposed process.

The post-doctoral fellow will work on the ultra high field platform of the CHU of Poitiers within the common laboratory I3M (XLIM) in the care of the regional project VIDALZ. This project aims to develop tools within the platform to analyze, integrate and contextualize multimodal imaging data for the diagnosis of Alzheimer's disease (AD).

The physiopathology of AD is complex, in parallel with amyloid plaques and neurofibrillary degeneration, there is metabolically impaired energy pathways, oxidative phosphorylation and glycolysis, which are involved in brain function. Several authors have thus shown a series of early metabolic dysregulation via an increase in phosphorylation at the origin of neuronal death.
Multinuclear Magnetic Resonance Spectroscopy at Ultra-high Field 7T (SRM 1H-31P) due to its increased spatial resolution makes it possible to study, in a non-invasive and non-irradiating way, the neurophysiology and neurobiochemistry of cerebral tissues. An MRS examination can be combined with cerebral MRI without additional risk for the patient. This multinucleus examination allows the collection of cerebral metabolic data in a non-invasive way.
PET imaging is used to assess the metabolic and cellular functioning of cerebral degeneration. One of the major advantages of PET is its quantitative aspect, which makes it possible to take precise and comparable measurements over time. In addition to being very sensitive, it has made it possible to highlight energy deficits which occur very early, even several decades before the appearance of the first symptoms in healthy subjects who are asymptomatic but present with risk factors. However, the information collected essentially relates only to glucose consumption. Hence the interest of being able to couple this investigation with spectroscopic data, which allows both to corroborate the information obtained by the two approaches, but also to broaden the scope of the parameters evaluated.