Clinical functional image analysis: artifact detection and reduction.

Rapid improvements in functional magnetic resonance neuroimaging technology have resulted in impressive advances in our understanding of structure/function relationships in the human brain. The application of this new technology to the understanding of human brain disease is currently limited by difficulties in extracting task-related signal change from signal intensity time series that have been contaminated by artifacts arising from various intrinsic and extrinsic sources. Effects induced by interscan head motion are a major source of these artifacts. The correction of these artifacts by registration of pairs of reconstructed images has been a focus of research for the past few years and there are now a number of effective means to compensate for this source of noise. This paper discusses issues concerning the prevention and correction of interscan head motion as well as other sources of error variation in fMRI time series.