Author(s)

Calvin W. L. Chin, Scott Semple, Tamir Malley, Audrey C. White, Saeed Mirsadraee, Peter J. Weale, Sanjay Prasad, David E. Newby, Marc R. Dweck

ISBN

Publication year

2013

Periodical

European Heart Journal Cardiovascular Imaging

Periodical Number

Volume

Pages

Author Address

Full version

Aims To determine the optimal T1 mapping approach to assess myocardial fibrosis at 3T.

Methods and results T1 mapping was performed at 3T using the modified look-locker-inversion sequence in 20 healthy volunteers and 20 patients with aortic stenosis (AS). Pre- and post-contrast myocardial T1, the partition coefficient (λ; ΔRmyocardium/ΔRblood, where ΔR = 1/post-contrast T1 − 1/pre-contrast T1), and extracellular volume fraction [ECV; λ (1 − haematocrit)] were assessed. After establishing the optimal time point and myocardial region for analysis, we compared the reproducibility of these T1 measures and their ability to differentiate asymptomatic patients with AS from healthy volunteers. There was no segmental variation across the ventricle in any of the T1 measures evaluated. λ and ECV did not vary with time, while post-contrast T1 was relatively constant between 15 and 30 min. Thus, mid-cavity myocardium at 20 min was used for subsequent analyses. ECV displayed excellent intra-, inter-observer, and scan–rescan reproducibility [intra-class correlation coefficients (ICC) 1.00, 0.97, and 0.96, respectively], as did λ (ICC 0.99, 0.94, 0.93, respectively). Moreover, ECV and λ were both higher in patients with AS compared with controls (ECV 28.3 ± 1.7 vs. 26.0 ± 1.6%, P < 0.001; λ 0.46 ± 0.03 vs. 0.44 ± 0.03, P = 0.02), with the former offering improved differentiation. In comparison, scan–rescan reproducibilities for pre- and post-contrast myocardial T1 were only modest (ICC 0.72 and 0.56) with no differences in values observed between cases and controls (both P> 0.05).

Conclusions ECV appears to be the most promising measure of diffuse myocardial fibrosis at 3T based upon its superior reproducibility and ability to differentiate disease from health.