Synopsis

Recent decades have witnessed an increasing number of large imaging studies. In particular for organizations with their own imaging equipment, configuring a system to collect, manage, process, review those datasets is still complicated, and relying on cloud-based solutions, albeit promising, is not always possible. We present a practical model aimed at improved quality control and guided by principles including user involvement, lightweight footprint, modularity and facilitated data sharing. This model gave rise to an ecosystem of independently reusable tools shaped around XNAT. This paradigm is scalable to the general community of researchers working with large imaging datasets.