van Hees Susan V, Schilder Martin B, Keyser Alexandra, Sbrizzi Alessandro, Kleinloog Jordi P D, Boon Wouter P C
Copernicus Institute of Sustainable Development, Innovation Studies Section, Utrecht University, the Netherlands.
Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.
Eur J Radiol Open. 2025 May 8;14:100658. doi: 10.1016/j.ejro.2025.100658. eCollection 2025 Jun.
MRI waitlists and discomfort from long scanning sessions are significant problems in clinical radiology. Novel multiparametric quantitative MRI techniques (qMRI) for radiological imaging enable acquisition of full-brain data within minutes to address these problems. While technical and clinical work is advancing, there has been limited research on implementing fast qMRI. This paper aims to identify implementation factors and scenarios within a healthcare setting facing rising demand, staff shortages, and limited capacity of MRI systems.
The paper reports on data collected using qualitative methods: 1) Interviews and guided discussions, 2) co-creation workshop. Both steps involved key representatives with various backgrounds and expertise, such as radiologists, lab technicians, insurers, and patients.
Workshop participants visualised current and future workflows, which helped articulate implementation factors for qMRI. Supply and demand in MRI will change with increased accessibility and shortened timeslots. Three implementation scenarios came forward: 1) stable deployment, 2) extension to conducting more complex diagnostic exams, and 3) (more) preventive screening.
This paper demonstrates challenges, solutions, and opportunities for successfully implementing fast qMRI in the clinic, and five lessons for adoption in the clinic: 1) importance of balancing perfectionism with confidence when it comes to clinicians' expectations, 2) good use of Artificial Intelligence, 3) considering a learning curve associated with implementation, 4) regarding competing technologies, and 5) including patients' experiences. Future research should investigate salient issues regarding future of AI in radiology and for moving imaging practices out of the clinic.
在临床放射学中，磁共振成像（MRI）等候名单以及长时间扫描带来的不适是重大问题。用于放射成像的新型多参数定量MRI技术（qMRI）能够在数分钟内获取全脑数据，以解决这些问题。虽然技术和临床工作在不断推进，但关于实施快速qMRI的研究却很有限。本文旨在确定在医疗需求不断增加、人员短缺且MRI系统容量有限的医疗环境中的实施因素和场景。
本文报告了使用定性方法收集的数据：1）访谈和引导式讨论，2）共创研讨会。这两个步骤都涉及了具有不同背景和专业知识的关键代表，如放射科医生、实验室技术人员、保险公司人员和患者。
研讨会参与者设想了当前和未来的工作流程，这有助于阐明qMRI的实施因素。随着可及性的提高和扫描时段的缩短，MRI的供需将会发生变化。出现了三种实施场景：1）稳定部署，2）扩展到进行更复杂的诊断检查，3）（更多的）预防性筛查。
本文展示了在临床中成功实施快速qMRI的挑战、解决方案和机遇，以及在临床中采用的五点经验教训：1）在临床医生的期望方面，平衡完美主义与信心的重要性，2）善用人工智能，3）考虑与实施相关的学习曲线，4）考虑竞争技术，5）纳入患者体验。未来的研究应调查放射学中人工智能未来的突出问题，以及将成像实践带出临床的问题。
