面向医学影像的智能云存储网关
An Intelligent Cloud Storage Gateway for Medical Imaging
摘要：
从历史上看，医学成像存储库一直由室内基础设施支持。然而，在过去的几十年中，诊断成像程序的数量不断增加，带来了与存储量、数据冗余和可用性相关的若干挑战。云平台专注于通过互联网提供硬件和软件服务，成为存储库外包的一个有吸引力的解决方案。虽然这一备选办法可能带来财政和技术利益，但也提出了新的挑战。在医学成像场景中，通信延迟是一个关键问题，仍然阻碍着这种范式的采用。本文提出了一种优化数据访问时间的智能云存储网关。这是通过一个新的高速缓存架构，结合静态规则和模式识别的驱逐和预取。通过对真实世界数据集的模拟获得的评估结果表明，缓存命中率可以达到80%左右，图像检索时间减少了60%以上。结合使用驱逐和预取的政策提出可以显着减少通信延迟，即使使用一个小的缓存相比，总大小的存储库。除了提升服务表现外，拟议的系统亦能配合不同院校的特定工作流程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
