Iulia A Popescu1, Konrad Werys2, Qiang Zhang2, Henrike Puchta2, Evan Hann2, Elena Lukaschuk2, Vanessa M Ferreira2, Stefan K Piechnik2. 1. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom. Electronic address: iulia.popescu@cardiov.ox.ac.uk. 2. Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Abstract
BACKGROUND: Cardiovascular magnetic resonance T1-mapping is increasingly used for tissue characterization, commonly based on Modified Look-Locker Inversion recovery (MOLLI). However, there are numerous MOLLI variants with differing normal ranges. This lack of standardization presents confusion and difficulty in inter-center comparisons, hindering widespread adoption of T1-mapping. METHODS: To address this, we performed a structured literature search for native left ventricular myocardial T1-mapping in healthy humans measured using MOLLI variants at 1.5 and 3 Tesla, across scanner vendors. We then used k-means clustering to structure normal MOLLI-T1 values according to magnetic field strength, and investigated correlations between common imaging parameters: repetition time (TR), echo time (TE), flip angle (FA). RESULTS: We analyzed data from 2207 healthy controls in 76 independent reports. Normal MOLLI-T1 standard deviations varied by 11-fold, and dependencies on TE, TR, and FA differed between 1.5 T and 3 T, thwarting meaningful T1 standardization even within a single field strength, including the use of Z-score. However, divergent MOLLI-T1 norms may be structured using data clustering. For 1.5 T, two clusters emerged: Cluster11.5T: T1 = 958 ± 16 ms (n = 1280); Cluster21.5T: T1 = 1027 ± 19 ms (n = 386). For 3 T, three clusters emerged: Cluster13T: T1 = 1160 ± 21 ms (n = 330); Cluster23T: T1 = 1067 ± 18 ms (n = 178); Cluster33T: T1 = 1227 ± 19 ms (n = 41). We then propose the concept of an online calculator for assigning local norms to a known MOLLI-T1 cluster, allowing benchmarking against published norms. CONCLUSION: Clustered structuring allows T1 standardization of widely-divergent MOLLI variants, benchmarking local norms (usually based on smaller samples) against published norms (larger samples). This may increase confidence and quality control in method implementation, facilitating wider clinical adoption of T1-mapping.
BACKGROUND: Cardiovascular magnetic resonance T1-mapping is increasingly used for tissue characterization, commonly based on Modified Look-Locker Inversion recovery (MOLLI). However, there are numerous MOLLI variants with differing normal ranges. This lack of standardization presents confusion and difficulty in inter-center comparisons, hindering widespread adoption of T1-mapping. METHODS: To address this, we performed a structured literature search for native left ventricular myocardial T1-mapping in healthy humans measured using MOLLI variants at 1.5 and 3 Tesla, across scanner vendors. We then used k-means clustering to structure normal MOLLI-T1 values according to magnetic field strength, and investigated correlations between common imaging parameters: repetition time (TR), echo time (TE), flip angle (FA). RESULTS: We analyzed data from 2207 healthy controls in 76 independent reports. Normal MOLLI-T1 standard deviations varied by 11-fold, and dependencies on TE, TR, and FA differed between 1.5 T and 3 T, thwarting meaningful T1 standardization even within a single field strength, including the use of Z-score. However, divergent MOLLI-T1 norms may be structured using data clustering. For 1.5 T, two clusters emerged: Cluster11.5T: T1 = 958 ± 16 ms (n = 1280); Cluster21.5T: T1 = 1027 ± 19 ms (n = 386). For 3 T, three clusters emerged: Cluster13T: T1 = 1160 ± 21 ms (n = 330); Cluster23T: T1 = 1067 ± 18 ms (n = 178); Cluster33T: T1 = 1227 ± 19 ms (n = 41). We then propose the concept of an online calculator for assigning local norms to a known MOLLI-T1 cluster, allowing benchmarking against published norms. CONCLUSION: Clustered structuring allows T1 standardization of widely-divergent MOLLI variants, benchmarking local norms (usually based on smaller samples) against published norms (larger samples). This may increase confidence and quality control in method implementation, facilitating wider clinical adoption of T1-mapping.
Authors: Qiang Zhang; Konrad Werys; Iulia A Popescu; Luca Biasiolli; Ntobeko A B Ntusi; Milind Desai; Stefan L Zimmerman; Dipan J Shah; Kyle Autry; Bette Kim; Han W Kim; Elizabeth R Jenista; Steffen Huber; James A White; Gerry P McCann; Saidi A Mohiddin; Redha Boubertakh; Amedeo Chiribiri; David Newby; Sanjay Prasad; Aleksandra Radjenovic; Dana Dawson; Jeanette Schulz-Menger; Heiko Mahrholdt; Iacopo Carbone; Ornella Rimoldi; Stefano Colagrande; Linda Calistri; Michelle Michels; Mark B M Hofman; Lisa Anderson; Craig Broberg; Flett Andrew; Javier Sanz; Chiara Bucciarelli-Ducci; Kelvin Chow; David Higgins; David A Broadbent; Scott Semple; Tarik Hafyane; Joanne Wormleighton; Michael Salerno; Taigang He; Sven Plein; Raymond Y Kwong; Michael Jerosch-Herold; Christopher M Kramer; Stefan Neubauer; Vanessa M Ferreira; Stefan K Piechnik Journal: Int J Cardiol Date: 2021-01-31 Impact factor: 4.164
Authors: Qiang Zhang; Matthew K Burrage; Elena Lukaschuk; Mayooran Shanmuganathan; Iulia A Popescu; Chrysovalantou Nikolaidou; Rebecca Mills; Konrad Werys; Evan Hann; Ahmet Barutcu; Suleyman D Polat; Michael Salerno; Michael Jerosch-Herold; Raymond Y Kwong; Hugh C Watkins; Christopher M Kramer; Stefan Neubauer; Vanessa M Ferreira; Stefan K Piechnik Journal: Circulation Date: 2021-07-07 Impact factor: 29.690