Functional Magnetic Resonance Imaging (fMRI) is a technique that may help us achieve a greater understanding of how the human brain functions. In fMRI, an MRI scanner is used to detect regional changes in cerebral metabolism or in blood flow, volume or oxygenation in response to brain activity. Changes in these physiological parameters lead to very small, yet detectable changes in MR images. Because the changes related to brain activity are small, fMRI is highly susceptible to sources of artifactual signal change, for example, physiological fluctuations, hardware instability and head movement. Here, we examine the challenge of correcting for head movement. Most movement correction algorithms have two basic steps - estimation of movement and correction of movement (interpolation). We have studied the error in both steps. We have developed a Fourier domain approach to reduce interpolation error for in-plane movements. For through-plane movements, we have developed a theoretical framework, based on sampling theory, to understand the sources of error and have devised acquisition and processing methods for reducing errors. Our approaches differ from commonly used approaches in that we have explicitly considered the details of image acquisition in development of methods for the correction of movement in fMRI.

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