Optimized Knowledge-Based Motion Correction of Fmri Time-Series Using Parallel Algorithms

The structure of an fMRI time series coregistration algorithm can be divided into modules (preprocessing, minimization procedure, interpolation method, cost function), for each of which there are many different approaches. In our study we implemented some of the most recent techniques and compared their combinations with regard to both registration accuracy and runtime performance. Bidirectional inconsistency and difference image analysis served as quality measures. The result shows that with an appropriate choice of methods realignment results can be improved by far compared with standard solutions. Finally, an automatic parameter adaptation method was incorporated. Additionally, the algorithm was implemented to run on a distributed 48 processor PC cluster, surpassing the performance of conventional applications running on high end workstations.