Post mortem inference of the human brain microstructure using ultra-high field magnetic resonance imaging with strong gradients

Abstract : The aim of ultra-high field strength (≥7T) and ultra-strong gradient systems (≥300mT/m) is to go beyond the millimeter resolution imposed at lower field and to reach the mesoscopic scale in neuroimaging. This scale is essential to understand the link between brain structure and function. However, despite recent technological improvements of clinical UHF-MRI, gradient systems remain too limited to reach this resolution. This thesis aims at answering the need for mapping the human brain at a mesoscopic scale by the study of post mortem samples. An alternative approach has been developed, based on the use of preclinical systems equipped with ultra-high fields (7T/11.7T) and strong gradients (780mT). After its extraction and fixation at Bretonneau University Hospital (Tours), an entire human brain specimen was scanned on a 3T clinical system, before separating its two hemispheres and cutting each hemisphere into seven blocks that could fit into the small bore of an 11.7T preclinical system. An MRI acquisition protocol targeting a mesoscopic resolution was then set up at 11.7T. This protocol, including anatomical, quantitative, and diffusion-weighted sequences, was validated through the study of two key structures: the hippocampus and the brainstem. From the high resolution anatomical and diffusion dataset of the human hippocampus, it was possible to segment the hippocampal subfields, to extract the polysynaptic pathway, and to observe a positive gradient of connectivity and neuritic density in the posterior-anterior direction of the hippocampal formation. The use of advanced microstructural models (NODDI) also highlighted the potential of these techniques to reveal the laminar structure of the Ammon’s horn. A high resolution anatomical and diffusion MRI dataset was obtained from the human brainstem with an enhanced resolution of a hundred micrometers. The segmentation of 53 of its 71 nuclei was performed at the Bretonneau University Hospital, making it the most complete MR-based segmentation of the human brainstem to date. Major white matter bundles were reconstructed, as well as projections of the locus coeruleus, a structure known to be impaired in Parkinson’s disease. Buoyed by these results, a dedicated acquisition campaign targeting the entire left hemisphere was launched for total scan duration of 10 months. The acquisition protocol was performed at 11.7T and included high resolution anatomical sequences (100/150μm) as well as 3D diffusion-weighted sequences (b=1500/4500/8000 s/mm², 25/60/90 directions, 200μm). In addition, T1-weighted inversion recovery turbo spin echo scans were performed at 7T to further investigate the myeloarchitecture of the cortical ribbon at 300µm, revealing its laminar structure. A new method to automatically segment the cortical layers was developed relying on a Gaussian mixture model integrating both T1-based myeloarchitectural information and diffusion-based cytoarchitectural information. The results gave evidence that the combination of these two contrasts highlighted the layers of the visual cortex, the myeloarchitectural information favoring the extraction of the outer layers and the neuritic density favoring the extraction of the deeper layers. Finally, the analysis of the MRI dataset acquired at 11.7T on the seven blocks required the development of a preprocessing pipeline to correct artifacts and to reconstruct the entire hemisphere using advanced registration methods. The aim was to obtain an ultra-high spatio-angular resolution MRI dataset of the left hemisphere, in order to establish a new mesoscopic post mortem MRI atlas of the human brain, including key information about its structure, connectivity and microstructure.
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Justine Beaujoin. Post mortem inference of the human brain microstructure using ultra-high field magnetic resonance imaging with strong gradients. Medical Imaging. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLS448⟩. ⟨tel-01977910v1⟩

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