Mapping tissue inhomogeneity in acute myocarditis: a novel analytical approach to quantitative myocardial edema imaging by T2-mapping

Journal of Cardiovascular Magnetic Resonance

Research

Guido Michels¹  Bettina Baeßler²  Anastasia Dick²  David Maintz²  Alexander C. Bunck²  Frank Schaarschmidt³  Christian Stehning⁴  Bernhard Schnackenburg⁵ 

[1] Department III of Internal Medicine, Heart Centre, University Hospital of Cologne, Cologne, Germany;Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne, Germany;Institute of Biostatistics, Faculty of Natural Sciences, Leibniz Universität Hannover, Hannover, Germany;Philips Research, Hamburg, Germany;Philips, Healthcare Germany, Hamburg, Germany;

关键词: Cardiovascular magnetic resonance;    Tissue characterization;    Acute myocarditis;    T2-mapping;    Myocardial edema;   

DOI  :  10.1186/s12968-015-0217-y

 received in 2015-09-15, accepted in 2015-12-09,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundThe purpose of the present study was to investigate the diagnostic value of T2-mapping in acute myocarditis (ACM) and to define cut-off values for edema detection.MethodsCardiovascular magnetic resonance (CMR) data of 31 patients with ACM were retrospectively analyzed. 30 healthy volunteers (HV) served as a control. Additionally to the routine CMR protocol, T2-mapping data were acquired at 1.5 T using a breathhold Gradient-Spin-Echo T2-mapping sequence in six short axis slices. T2-maps were segmented according to the 16-segments AHA-model and segmental T2 values as well as the segmental pixel-standard deviation (SD) were analyzed.ResultsMean differences of global myocardial T2 or pixel-SD between HV and ACM patients were only small, lying in the normal range of HV. In contrast, variation of segmental T2 values and pixel-SD was much larger in ACM patients compared to HV. In random forests and multiple logistic regression analyses, the combination of the highest segmental T2 value within each patient (maxT2) and the mean absolute deviation (MAD) of log-transformed pixel-SD (madSD) over all 16 segments within each patient proved to be the best discriminators between HV and ACM patients with an AUC of 0.85 in ROC-analysis. In classification trees, a combined cut-off of 0.22 for madSD and of 68 ms for maxT2 resulted in 83 % specificity and 81 % sensitivity for detection of ACM.ConclusionsThe proposed cut-off values for maxT2 and madSD in the setting of ACM allow edema detection with high sensitivity and specificity and therefore have the potential to overcome the hurdles of T2-mapping for its integration into clinical routine.

【 授权许可】

CC BY   
© Baeßler et al. 2015