The Analysis of Functional Neuroimaging (AFNI) is a widely adopted software package in the fMRI data analysis community. For many types of analysis pipelines, one important step is to register a subject's image to a pre-defined template so different images can be compared within a normalized coordination system. Although a 12-point affine transformation works fine for some standard cases, it is usually found insufficient for voxelwise types of analyses. This is especially challenging if the subject has brain atrophy due to some kinds of neurological condition such as Parkinson's disease. The 3dQwarp code in AFNI is a non-linear image registration procedure that overcomes the drawbacks of a canonic affine transformation. However, the existing OpenMP instrumentation in 3dQwarp is not efficient for warping at an ultra fine level, and the constant trip counts of the iterative algorithm also hurts the accuracy. Based on the profiling and benchmark analysis, we improve the parallel efficiency by the optimization of its OpenMP structure and obtain about 2x speedup for normalized workload. With the incorporation of a convergence criteria, we are able to perform warping at a much finer level beyond the default threshold and achieve about 20% improvement in term of Pearson correlation.
