Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g.
lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally
provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An
alternative solution is to access the required computation power over the internet from a cloud computing service, which
is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation
resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this
paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We
parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the
standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a
very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce
implementation might become necessary in the future. All the experiments for this paper, including development and
testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.