Exploring scenarios for implementing fast quantitative MRI
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.
Authors
- Alessandro Sbrizzi (ORCID: https://orcid.org/0000-0003-3276-4542)
- Susan van Hees (ORCID: https://orcid.org/0000-0001-8057-2356)
- Jordi P. D. Kleinloog (ORCID: https://orcid.org/0000-0001-5861-9656)
- Andreas Keyser (ORCID: https://orcid.org/0000-0001-5922-5288)
- Wouter Boon (ORCID: https://orcid.org/0000-0003-1218-193X)
- Martin Bastiaan Schilder (ORCID: https://orcid.org/0009-0003-4058-4928)
Institutions
- University Medical Center Utrecht (NL)
- Institute for Sustainable Development (SI)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1016/j.ejro.2025.100658
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek