Developing a Protocol for Detection of Axonal Injury in Rats Using Diffusion Tensor Magnetic Resonance Imaging - Evaluating large volumes of data to find microscopic injuries
Examensarbete för masterexamen
Biomedical engineering (MPBME), MSc
Traumatic brain injury is a serious injury that is all too common. Commonly traumatic brain injury includes stretching of the axons which is due to the movement of the brain. Today it is not possible to detect axonal injury using conventional imaging techniques, but the possibility to do so would provide superior screenings for injury and guide treatment of patients. The goal of this thesis was to examine the possibility of creating a new protocol for detection of such injuries. Diffusion tensor magnetic resonance images from animal studies on rats, five exposed and four normals, have been studied. The main focus of this thesis was to attempt to differentiate the datasets and find injuries located in the corpus callosum. The study began with examining the results of a previous study carried out on the same dataset. This examination was followed by evaluating fractional anisotropy and tractography, with the conclusion that fractional anisotropy can be useful but possibly insufficient for detection of axonal injuries when the spatial resolution is too high. Since fractional anisotropy stays the same in points where all eigenvalues increase or decreases equally, injuries might go undetected. Tractography suffers from a few problems underlying with DT-MRI itself, namely the poor ability to resolve crossing fibers, but can be a useful tool in smaller brain regions. Further attempts at spatial normalization and amplitude normalization was made to facilitate a voxel-wise analysis of the brain, comparing each voxel of the traumatized and aligned brains with the voxels of healthy animals. The results did not show significant differences between the healthy and injured animals. Attempts at using classifiers to differentiate between the animals were made, using both neural networks and a linear discriminant analysis classifier. For the neural network classifier this seems promising, but the number of animals in the study was not large enough to be able to perform a complete evaluation. Suggestions on how to improve detection of axonal injuries and verifying the possibility to do so are presented; such as using phantoms to be able to place controlled injuries simulating axonal injuries and comparing images before and after introducing the injury. Such an approach would facilitate detection of diffuse axonal injury by ensuring that a detected difference stems from the injury alone.
Medicinteknik , Hållbar utveckling , Transport , Medical Engineering , Sustainable Development , Transport