Characterization and compensation of systematic noise in functional magnetic resonance imaging.

TL;DRAbstract

Functional magnetic resonance imaging (fMRI) has emerged as an important tool for noninvasive neuroscientific research. A limit to its effectiveness, however, is the presence of systematic noise that can obscure neuronal activation. Systematic noise in fMRI has a temporal and/or spatial structure, as opposed to additive random Gaussian white noise (e.g. thermal fluctuations). Several examples are low frequency signal drifts, head motion, physiological noise, and spontaneous neuronal events. These systematic noise sources are generally multiplicative and depend on the signal strength. As the fMRI signal is increased, by increasing voxel size or field strength, these noise sources may dominate the thermal noise, and determine the effective signal-to-noise ratio of a functional imaging experiment. Thus, understanding these noise sources and how to mitigate their effects is an important step in maximizing the potential of functional MRI as a neuro-imaging tool. This dissertation investigat

Chat with Paper

AI Agents for this Paper