Inter-vendor standardisation and quality assurance of quantitative cardiovascular magnetic resonance (CMR) T1-mapping for clinical practice and trials

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Quantitative CMR (qCMR) T1-mapping is named one of the most innovative technologies to evaluate heart disease by the European Society of Cardiology. T1-mapping provides pixel-wise characterisation of body tissue, beyond what conventional MRI offers, and has been shown to detect subclinical cardiac and liver disease. Each tissue type has a normal T1 range, deviation from which may indicate disease. However, there are many T1- mapping techniques, each with their own normal range on different scanners by different vendors. This lack of standardisation limits inter-centre comparability and wider roll-out for clinical use. We have successfully developed a cardiac T1-mapping technique - the Shortened Modified Look-Locker inversion recovery (ShMOLLI) - now in 5 international guidelines, with >60 papers, ~6000 citations and 5 awards. It has the largest clinical evidence base for any single T1-mapping method, and is the designated T1-mapping technique in UK Biobank (100,000 datasets by 2021/2022). We have also recently published our unique approach to mapping standardisation for clinical trials (2021), and had garnered support letters from 4 industry partners (GE included), with >100 interested CMR sites worldwide. Our method currently operates on a single vendor platform (Siemens), but now ready for inter-vendor standardisation. Given substantial inter-vendor differences in technologies, achieving this is an ambitious feat; this requires significant buy-in and investment from industry partners, to provide vendor-specific method implementation on vendor proprietary systems. Upon solving this unmet clinical need, we expect to translate qCMR mapping into widespread use in the NHS, through the use of common norms and guidelines. This would also allow the building of a vendor-independent national imaging database, compatible with UK Biobank, to address important clinical questions through big data and artificial intelligence. This study will rely on recently purchased GE MR scanner as a platform to enable inter-vendor standardisation programme. This proposed project is aligned to MRC's remit in several areas: the translational impact of diagnostic and data analytics, to enable a future national imaging database that is aligned with the UK Biobank big data, for further applications of artificial intelligence and machine learning, towards precision medicine. The project hinges on interdisciplinary skills and ways of working, between biomedical imaging, MR physics, physicians and industry partners, and will enable researcher mobility between sectors of health and the physical sciences.