Title: "Diffusion MRI and DKI to evaluate the axonal degeneration in vivo: general framework and focus on some processing tools"
Speaker: Stéphanie Guérit (ICTEAM/ELEN, UCL)
Location: "Shannon" Seminar Room (a105) Place du Levant 3, Maxwell Building, 1st floor
Date / Time (duration): January 22, 10h00 (~ 40')
Abstract: A lot of incidents in everyday life can lead to spinal cord injuries: motor vehicle accidents, falls, sport injuries, etc. In some cases, the consequences are superficial or reversible but they can also have an important impact on the quality of life of the patients by the permanent loss of functionality. At this time, the process of axonal degeneration in the central nervous system (CNS), responsible for this functional loss, is not well known. The current clinical methods to evaluate the extent of the damage are based on the sensorial perception of the patient and its ability to contract some of his muscles. There exists no objective method based on the observation of physiological processes.
The main goal of my master thesis was to explore the possibility to use diffusion MRI to characterize the axonal degeneration after spinal cord injury. This is a non-invasive technique producing diffusion weighted images (i.e. images whose intensity of each voxel depends on the diffusion of water molecules inside). The degeneration of the nervous fibers modifies the properties of the axonal membrane and the myelin sheath leading to changes in the diffusion profile of water molecules. Three steps are considered: (i) the recovery of the displacement profile of water molecules from the MR signal, (ii) the extraction of relevant metrics from these profiles and (iii) the correspondence between these ones and the changes observed in the microstructure (histological analyses). The last step is essential to assess if the metrics are pertinent biomarkers.
The goal of this talk is to present an overview of the work realized during my master thesis with a particular focus on the processing step, i.e. the noise correction in MR images and the different diffusion models used to fit the data.
Last updated January 22, 2014, at 02:17 PM