|M.Sc Student||Hagai Tzafrir|
|Subject||Geometric Analysis for Multi-Modal MRI|
|Department||Department of Electrical Engineering||Supervisors||Full Professor Kimmel Ron|
|Clinical Professor Goldsher Dorith|
|Full Thesis text|
The rapid development in medical imaging produces a continuous stream of knowledge about pathologies and diseases, leading to new therapeutic procedures and discovering the complex structures of internal organs and their relation to health problems. Due to their non-invasive nature, medical imaging technologies play a central role in most medical diagnosis and treatment methodologies. These technologies accelerate healing and provide cost-efficient health-care tools.
In this work we present an analysis method to improve treatment procedures for a set of neurological pathologies, specifically essential tremor disorder. By combining anatomical and pathological knowledge with modern geometric analysis tools of MRI, an efficient focused ultrasound treatment procedure is introduced.
As a first step, we apply statistical geometric tools for in-vivo analysis of the brain. The brain's connectivity structure is interpreted based on diffusion-MRI. Next, mapping the brain structure onto a representative dictionary template allows us to locate regions of functional interest.
Finally, applying efficient statistical geometric tools to the DT-MRI, the brain's connectivity structure can be estimated and used to pin-point the location to be treated with the focused ultrasound (FUS).
We aim to utilize the potential benefit in personalized medicare, individually tailored treatment, and evidence-based decision making, for better understanding the effect of some treatments on known pathologies. Applying the resulting computational analysis routines to enhance and improve modern medical treatments is our ultimate goal in this research.