Keaveney Sam, McHugh Damien J, Rata Mihaela, Dragan Alina, Winfield Jessica M, Doran Simon J, Blackledge Matthew D, Scurr Erica, Koh Dow-Mu, Berks Michael, Gill Andrew B, Birchall Jonathan R, O'Connor James P B, King Alexander, Rennie Winston J, Gaba Suchi, Suresh Priya, Malcolm Paul, Davis Amy, Nilak Anjumara, Shah Aarti, Gandhi Sanjay, Albrizio Mauro, Pratt Guy, Cook Gordon, Hall Andrew, Roberts Sadie, Jenner Matthew, Brown Sarah, Kaiser Martin, Hubbard Cristinacce Penny L, Messiou Christina
MRI Unit, The Royal Marsden NHS Foundation Trust, London, SM2 5PT, United Kingdom.
Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, SW3 6JB, United Kingdom.
Br J Radiol. 2025 Aug 1;98(1172):1236-1244. doi: 10.1093/bjr/tqaf089.
Clinical translation of advanced MRI techniques can be hindered by the challenges of performing standardized multicentre imaging trials. This work aims to develop and demonstrate an automated tool for monitoring imaging protocol deviations, enabling corrective action to be taken.
A Python-based tool, integrated into the imaging repository XNAT, was developed to compare DICOM series with an agreed imaging protocol, highlighting missing series and parameter deviations. This was demonstrated through retrospective analysis of a prospectively acquired dataset from a ten-site whole-body (WB) MRI study of patients with multiple myeloma. The acquired data were compared to the relevant radiological guidelines and to the site-specific imaging protocols agreed for the study.
The rate of technical software failure was 0% across 174 examinations from 10 sites. The clinical guidelines were followed in 87.9% of examinations and compliance with the site-specific imaging protocol was greater than 75.0% for all parameters. Common deviations included number of averages for diffusion-weighted imaging (DWI) and repetition time for DWI and Dixon: 85.2%, 81.7%, and 75.1%, respectively. There was a statistically significant correlation between protocol compliance and overall exam radiological image quality.
Repository-integrated software is presented for automated monitoring of imaging protocol compliance to support standardization in multicentre studies and clinical translation.
This study presents a novel open-source repository-integrated software tool for automatically monitoring compliance with the expected imaging protocol. Standardized acquisition protocols are crucial in multicentre imaging studies and this tool has the potential to enhance research outcomes and support clinical translation.
开展标准化多中心成像试验面临诸多挑战,这可能会阻碍先进磁共振成像(MRI)技术的临床转化。本研究旨在开发并展示一种用于监测成像协议偏差的自动化工具,以便能够采取纠正措施。
开发了一种基于Python的工具,并将其集成到成像存储库XNAT中,用于将DICOM序列与商定的成像协议进行比较,突出显示缺失的序列和参数偏差。通过对一项针对多发性骨髓瘤患者的十中心全身(WB)MRI研究中前瞻性采集的数据集进行回顾性分析,对该工具进行了演示。将采集到的数据与相关放射学指南以及该研究商定的各中心特定成像协议进行了比较。
在来自10个中心的174次检查中,技术软件故障率为0%。87.9%的检查遵循了临床指南,所有参数对各中心特定成像协议的依从性均大于75.0%。常见偏差包括扩散加权成像(DWI)的平均次数以及DWI和狄克逊成像的重复时间,分别为85.2%、81.7%和75.1%。协议依从性与总体检查放射图像质量之间存在统计学显著相关性。
本文介绍了一种集成到存储库中的软件,用于自动监测成像协议依从性,以支持多中心研究和临床转化的标准化。
本研究提出了一种新型的开源存储库集成软件工具,用于自动监测对预期成像协议的依从性。标准化采集协议在多中心成像研究中至关重要,该工具具有改善研究结果并支持临床转化的潜力。