A data-parallel algorithm for iterative tomographic image reconstruction

In the tomographic imaging problem images are reconstructed from a set of measured projections. Iterative reconstruction methods are computationally intensive alternatives to the more traditional Fourier-based methods. Despite their high cost, the popularity of these methods is increasing because of the advantages they pose. Although numerous iterative methods have been proposed over the years, all of these methods can be shown to have a similar computational structure. This paper presents a parallel algorithm that we originally developed for performing the expectation maximization algorithm in emission tomography. This algorithm is capable of exploiting the sparsity and symmetries of the model in a computationally efficient manner. Our parallelization scheme is based upon decomposition of the measurement-space vectors. We demonstrate that such a parallelization scheme is applicable to the vast majority of iterative reconstruction algorithms proposed to date.