Decoupled automated rotational and translational registration for functional MRI time series data: The dart registration algorithm

Abstract

A rapid, in-plane image registration algorithm that accurately estimates and corrects for rotational and translational motion is described. This automated, one-pass method achieves its computational efficiency by decoupling the estimation of rotation and translation, allowing the application of rapid cross-correlation and cross-spectrum techniques for the determination of displacement parameters. k-space regridding and modulation techniques are used for image correction as alternatives to linear interpolation. The performance of this method was analyzed with simulations and echo-planar image data from both phantoms and human subjects. The processing time for image registration on a Hewlett-Packard 735/125 is 7.5 s for a 128 x 128 pixel image and 1.7 s for a 64 x 64 pixel image. Imaging phantom data demonstrate the accuracy of the method (mean rotational error, -0.09 degrees; standard deviation = 0.17 degrees; range, -0.44 degrees to +0.31 degrees; mean translational error = -0.035 pixels; standard deviation = 0.054 pixels; range, -0.16 to +0.06 pixels). Registered human functional imaging data demonstrate a significant reduction in motion artifacts such as linear trends in pixel time series and activation artifacts due to stimulus-correlated motion. The advantages of this technique are its noniterative one-pass nature, the reduction in image degradation as compared to previous methods, and the speed of computation.