  Historically, medical imaging repositories have been supported by indoor
infrastructures. However, the amount of diagnostic imaging procedures has
continuously increased over the last decades, imposing several challenges
associated with the storage volume, data redundancy and availability. Cloud
platforms are focused on delivering hardware and software services over the
Internet, becoming an appealing solution for repository outsourcing. Although
this option may bring financial and technological benefits, it also presents
new challenges. In medical imaging scenarios, communication latency is a
critical issue that still hinders the adoption of this paradigm. This paper
proposes an intelligent Cloud storage gateway that optimizes data access times.
This is achieved through a new cache architecture that combines static rules
and pattern recognition for eviction and prefetching. The evaluation results,
obtained through simulations over a real-world dataset, show that cache hit
ratios can reach around 80%, leading reductions of image retrieval times by
over 60%. The combined use of eviction and prefetching policies pro- posed can
significantly reduce communication latency, even when using a small cache in
comparison to the total size of the repository. Apart from the performance
gains, the proposed system is capable of adjusting to specific workflows of
different institutions.
