Automated Quality Control for Multi-Vendor, Multi-Centre Renal Imaging Studies

Motivation: It is critical that MRI data acquired in multi-site, multi-vendor studies conforms to a standardised acquisition protocol. Goal(s): To develop XNAT tools to highlight scans that do not conform to a specified protocol or are of insufficient quality, enabling rapid correction of errors before future scans. Approach: Multi-site DICOM data is uploaded to XNAT after acquisition, by integrating software tools with this database, investigators are informed if data does not conform. Results: DICOM-QC, a tool to automatically compare DICOM metadata to predefined values, and ImageSNR-QC to calculate image SNR, applied here to a multi-site kidney study. Impact: This work outlines two tools that integrate with XNAT, DICOM-QC and ImageSNR-QC, which can be used by any investigators running large studies to ensure uploaded data conforms to the study protocol, ensuring consistency over sites, vendors, and repeated longitudinal scans.