MOTION DETECTION AND CORRECTION IN FUNCTIONAL MR-IMAGING

Subject motion present during the time course of functional activation studies is a pervasive problem in mapping the spatial and temporal ch aracteristics of brain activity. In functional MRI (fMRI) studies, the observed signal changes are small. Therefore, it is crucial to reduce the effect of subject motion during the acquisition of image data in order to differentiate true brain activation from artifactual signal c hanges due to subject motion. We have adapted a technique for automati c motion detection and correction which is based on the ratio-variance minimization algorithm to the fMRI subject motion problem. This metho d was used for retrospective correction of subject motion in the acqui red data and resulted in improved functional maps. In this paper we ha ve designed and applied a series of tests to evaluate the performance of this technique which span the classes of image characteristics comm on to fMRI. These areas include tests of the accuracy and range of mot ion as well as measurement of the effect of image signal to noise rati o, focal activation, image resolution, and image coverage on the motio n detection system. Also, rye have evaluated the amount of residual mo tion remaining after motion correction, and the ability of this techni que to reduce the motion-induced artifacts and restore regions of acti vation lost due to subject motion. In summary, this method performed w ell in the range of image characteristics common for fMRI experiments, reducing residual motion to under 0.5 mm, and removed significant mot ion-induced artifacts while restoring true regions of activation. Moti on correction is expected to become a routine requirement in the analy sis of fMRI experiments. (C) 1996 Wiley-Liss, Inc.