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A project establishing quantitative MRI analysis has released a short video summarising the most important results achieved

Magnetic Resonance Imaging (MRI) is a commonly used medical scanning technique with more than 30 million scans performed per year in Europe. Despite their prevalence, MRI scans produce typically qualitative data and rely on visual inspection by a clinician to identify and diagnose pathologies.

Recently, new scanning methods have become available such as Electric Properties Tomography (EPT) and Magnetic Resonance Fingerprinting (MRF) which offer the ability to produce quantitative images that could be analysed by software or AI – making their results much more objective.

However, these new approaches have lacked the metrological validation required for clinical use.

The EMPIR project Quantitative MR-based imaging of physical biomarkers (18HLT05, QUIERO) has taken the first steps in providing this missing standardisation.

During the project EPT and MRF techniques were evaluated and algorithms for analysing the results from these developed and released as freeware (EPT, MRF). This allowed comparison of the performance of different EPT/MRF approaches under identical conditions and their characterisation against synthetic data.

In addition, the project developed new phantoms for EPT and MRF - mimicking real tissues - which were used to characterise the outputs of these types of scans in terms of repeatability and reproducibility.

The QUIERO project has now completed, and the consortium have released a video on their project page describing some of the most important outputs of the project – including the work on imaging the brain and heart using these techniques.

Dr Zilberti (INRiM) who coordinated the project commented on the work:

“QUIERO laid the foundation of a metrological approach to EPT and MRF and made our knowledge of such techniques deeper and wider. This final video provides evidence of the hard work done to make our tools and results available to everyone.”

This EMPIR project is co-funded by the European Union's Horizon 2020 research and innovation programme and the EMPIR Participating States.

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