
The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi‐institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application‐specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48–61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
Brain Mapping, Phantoms, Imaging, system consistency, Contrast Media, Reproducibility of Results, quality assurance, phantom, Models, Theoretical, Signal-To-Noise Ratio, Magnetic Resonance Imaging, Elasticity, Perfusion, quantitative, Reference Values, Health Sciences, Calibration, Image Processing, Computer-Assisted, Linear Models, Humans, Algorithms, Biomarkers
Brain Mapping, Phantoms, Imaging, system consistency, Contrast Media, Reproducibility of Results, quality assurance, phantom, Models, Theoretical, Signal-To-Noise Ratio, Magnetic Resonance Imaging, Elasticity, Perfusion, quantitative, Reference Values, Health Sciences, Calibration, Image Processing, Computer-Assisted, Linear Models, Humans, Algorithms, Biomarkers
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 155 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
