Case Studies

Clinical translation requires an XNAT instance that has hardened HIPAA-compliant security to manage patient data, rapid pipeline processing, and extensive connections with medical research scanners and PACS to facilitate querying of patient scan series and posting new data back to the clinical environment. With this technical workflow in place, emerging imaging research can be applied in real time to clinical patient care.

The wealth of imaging research that is becoming available has the ability to directly influence clinical care; likewise, patients receiving clinical care may elect to participate in ongoing research into their condition, even if they cannot directly benefit. Clinical imaging research includes both retrospective studies, which rely on batches of historic patient scans meeting some diagnostic criteria, and prospective studies, which rely on individual patients scans to be imported and often processed in real-time, sometimes during a surgical procedure. From an imaging informatics perspective, support for clinical research requires integration with clinical devices and information systems, careful compliance with regulatory requirements; and agility in moving between clinical and research data formats and protocols. XNAT's existing DICOM workflow, pipeline service, and a variety of standard clinical forms (e.g. Radiological reads, NIH Stroke scale) provide support for clinical imaging research. XNAT's overall security infrastructure is well-suited to clinical research, particularly in adhering to strict patient data privacy regulations such as HIPAA and CITI. Additionally, XNAT's built in ability to share subsets of data, and control sharing on a granular level, make it possible to quickly translate patient-specific clinical data into anonymized research data that can be grouped, analyzed, shared and published on.

Imaging-based research is becoming increasingly important to research institutions. New imaging technologies are yielding insights into the realms of cancer and disease pathology, and research funding is growing rapidly for imaging projects. The challenge for research institutions is to be able to manage this rapid growth of research data. Imaging data is exponentially larger than clinical data, and its analysis requires multiple rounds of computationally demanding processing. Moreover, the systems designed to store imaging data that are directly attached to scanners have not kept up with complex demands of imaging research. They can store files, but do little else, and offer no support for integrating imaging data with any associated subject metadata. This is why XNAT was created.

Imaging research and analysis is increasingly dependent on acquiring data from large numbers of subjects, which in turn means searching across wide geographical areas to find enough subjects that meet your study's criteria. One way to manage this is to collaborate with a number of research institutions to recruit and image subjects from. While this is economically more feasible (and friendlier to your subjects), it introduces a new host of challenges for study coordination: disparate scanning technologies and devices; non-uniform process for image acquisition and data handling; the challenge of aggregating all this data into a centralized system, and then managing access to this data across a large number of collaborators from outside your institution. XNAT has evolved to solve this problem.

Data sharing entails an investigator distributing his data, either openly, semi-openly, or in closed collaborations. Large NIH studies are required to share data and many smaller projects have realized the benefits of sharing. However, data sharing requires the use of an application that can give researchers control over multiple levels of access, and control which data is accessible by whom. With the increasing prevalence of sharing, XNAT is being used more frequently in this context. The ConnectomeDB, which distributes more than 2 petabytes of data for the Human Connectome Project, is a prime example.

The XNAT hardware setup at the University of Iowa is a robust enterprise class data storage system, serving multiple XNAT instances (a site-wide repository as well as an instance set up for the multi-site PREDICT-HD project). This information was gathered from a conversation between Adam Harding at Iowa and Chip Schweiss at WashU.