FMRI denoising and data analysis for medical applications

Abstract

Since its development in the early 1990s, functional MRI has emerged as a useful tool to explore the functional behavior of the human brain. Image processing for fMRI data analysis has been playing a very important role for determining which parts of the brain are activated by different types of stimuli. Functional magnetic resonance imaging (FMRI) data is severely contaminated by noise, in large part due to physiologic noise caused by respiratory and cardiac variations over time. This Final Year Project attempts to better characterize several noise correction and analysis techniques applied to FMRI data.