Standardization of T1-mapping in cardiovascular magnetic resonance using clustered structuring for benchmarking normal ranges. (1st March 2021)

• Record Type: Journal Article
• Authors: Popescu, Iulia A.; Werys, Konrad; Zhang, Qiang; Puchta, Henrike; Hann, Evan; Lukaschuk, Elena; Ferreira, Vanessa M.; Piechnik, Stefan K.
• Is Part Of: International journal of cardiology. Volume 326(2021)
• Page Start: 220
• Page End: 225
• Publication Date: 2021-03-01
• Subjects: T1-mapping -- MOLLI -- Normal controls -- Native T1 -- Standardization -- Clustering; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2020.10.041
• Languages: English
• ISSNs: 0167-5273

Abstract

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. Highlights: Normal myocardial T1 values vary widely across different MOLLI T1-mapping methods. Clustered structuring via an online calculator is practical for T1 standardization. Clustered structuring allows benchmarking of local norms against published norms. Clustered structuring increases confidence and quality in T1-mapping implementation.