The ability to correct certain types of MR images for motion artifacts from the raw data alone by iterative optimization of an image quality measure has recently been demonstrated. In the first study on a large data set of clinical images, we showed that such an autocorrection technique significantly improved the quality of clinical rotator cuff images, and performed almost as well as navigator echo correction while never degrading an image. One major criticism of such techniques is that they are computationally intensive, and reports of the processing time required have ranged form a few minutes to tens of minutes per slice. In this paper we describe a variety of improvements to our algorithm as well as approaches to correct sets of adjacent slices efficiently. The resulting algorithm is able to correct 256x256x20 clinical shoulder data sets for motion at an effective rate of 1 second/image on a standard commercial workstation. Future improvements in processor speeds and/or the use of specialized hardware will translate directly to corresponding reductions in this calculation time.