Abstract #0541

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

Alexander J Daniel¹, Martin Craig^1,2, David L Thomas^3,4,5, Iosif Mendichovszky^6,7, Steven Sourbron⁸, David M Morris⁹, Andrew N Priest^6,7, Charlotte E Buchanan¹, and Susan T Francis^1,2

¹Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, ²NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, United Kingdom, ³Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁴Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁵Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁶Department of Radiology, Addenbrooke’s Hospital, Cambridge, United Kingdom, ⁷Department of Radiology, University of Cambridge, Cambridge, United Kingdom, ⁸Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom, ⁹Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom

Synopsis

Keywords: Software Tools, Software Tools, Standardisation, Quality Control

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.

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