European Radiology

, Volume 27, Issue 12, pp 5169–5178 | Cite as

Re-evaluation of a novel approach for quantitative myocardial oedema detection by analysing tissue inhomogeneity in acute myocarditis using T2-mapping

  • Bettina Baeßler
  • Frank Schaarschmidt
  • Melanie Treutlein
  • Christian Stehning
  • Bernhard Schnackenburg
  • Guido Michels
  • David Maintz
  • Alexander C. Bunck
Cardiac
  • 164 Downloads

Abstract

Objectives

To re-evaluate a recently suggested approach of quantifying myocardial oedema and increased tissue inhomogeneity in myocarditis by T2-mapping.

Methods

Cardiac magnetic resonance data of 99 patients with myocarditis were retrospectively analysed. Thirthy healthy volunteers served as controls. T2-mapping data were acquired at 1.5 T using a gradient-spin-echo T2-mapping sequence. T2-maps were segmented according to the 16-segments AHA-model. Segmental T2-values, segmental pixel-standard deviation (SD) and the derived parameters maxT2, maxSD and madSD were analysed and compared to the established Lake Louise criteria (LLC).

Results

A re-estimation of logistic regression models revealed that all models containing an SD-parameter were superior to any model containing global myocardial T2. Using a combined cut-off of 1.8 ms for madSD + 68 ms for maxT2 resulted in a diagnostic sensitivity of 75% and specificity of 80% and showed a similar diagnostic performance compared to LLC in receiver-operating-curve analyses. Combining madSD, maxT2 and late gadolinium enhancement (LGE) in a model resulted in a superior diagnostic performance compared to LLC (sensitivity 93%, specificity 83%).

Conclusions

The results show that the novel T2-mapping-derived parameters exhibit an additional diagnostic value over LGE with the inherent potential to overcome the current limitations of T2-mapping.

Key Points

A novel quantitative approach to myocardial oedema imaging in myocarditis was re-evaluated.

The T2-mapping-derived parameters maxT2 and madSD were compared to traditional Lake-Louise criteria.

Using maxT2 and madSD with dedicated cut-offs performs similarly to Lake-Louise criteria.

Adding maxT2 and madSD to LGE results in further increased diagnostic performance.

This novel approach has the potential to overcome the limitations of T2-mapping.

Keywords

Myocarditis T2 mapping Oedema imaging Tissue inhomogeneity Lake Louise criteria 

Abbreviations

AIC

Akaike information criterion

AUC

Area under the curve

BSA

Body surface area

bSSFP

Balanced steady-state free-precession

CAD

Coronary artery disease

CMR

Cardiovascular magnetic resonance

ECG

Electrocardiogram

ED

End diastolic

EF

Ejection fraction

EGEr

Early gadolinium enhancement ratio

EMB

Endomyocardial biopsy

ES

End systolic

FA

Flip angle

GraSE

Gradient Spin Echo T2 mapping sequence

IQR

Interquartile range

LGE

Late gadolinium enhancement

LLC

Lake Louise criteria

LV

Left ventricle

MAD

Mean absolute deviation

madSD

MAD of segmental pixel-SD values

madlSD

Loge-transformed version of madSD

madT2

MAD of segmental T2 values

maxSD

Maximum segmental pixel-SD value

maxT2

Maximum segmental T2 value

MLE

Maximum likelihood estimator

Pixel-SD

Segmental pixel-standard deviation of T2 values

ROC

Receiver operating curve

ROI

Region of interest

SAX

Short axis

SD

Standard deviation

T

Tesla

T2 BB

T2 black blood

TE

Echo time

TnT

Troponin T

TR

Repetition time

Notes

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Bettina Baeßler.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Dr. Stehning and Dr. Schnackenburg are employees of Philips Research and Philips Healthcare, respectively.

Funding

The authors state that this work has not received any funding.

Statistics and biometry

Dr. Frank Schaarschmidt kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all healthy volunteers in this study.

Written informed consent for the patients was waived by the Institutional Review Board due to the retrospective nature of the patient study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Baeßler B, Schaarschmidt F, Dick A, et al (2015) Mapping tissue inhomogeneity in acute myocarditis: a novel analytical approach to quantitative myocardial enema imaging by T2-mapping. J Cardiovasc Magn Reson: Official Journal of the Society for Cardiovascular Magnetic Resonance 17:115. doi:  10.1186/s12968-015-0217-y.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2017_4894_MOESM1_ESM.docx (199 kb)
ESM 1 (DOCX 199 kb)

References

  1. 1.
    Greulich S, Ferreira VM, Dall'Armellina E, Mahrholdt H (2015) Myocardial inflammation-are we there yet? Curr Cardiovasc Imaging Rep. doi: 10.1007/s12410-015-9320-6 PubMedPubMedCentralGoogle Scholar
  2. 2.
    Park CH, Choi E-Y, Greiser A et al (2013) Diagnosis of acute global myocarditis using cardiac MRI with quantitative t1 and t2 mapping: case report and literature review. Korean J Radiol. doi: 10.3348/kjr.2013.14.5.727 Google Scholar
  3. 3.
    Friedrich MG, Sechtem U, Schulz-Menger J et al (2009) Cardiovascular magnetic resonance in myocarditis: a JACC white paper. J Am Coll Cardiol. doi: 10.1016/j.jacc.2009.02.007 PubMedCentralGoogle Scholar
  4. 4.
    Francone M, Chimenti C, Galea N et al (2014) CMR sensitivity varies with clinical presentation and extent of cell necrosis in biopsy-proven acute myocarditis. J Am Coll Cardiol Img. doi: 10.1016/j.jcmg.2013.10.011 Google Scholar
  5. 5.
    Ferreira VM, Piechnik SK, Dall'Armellina E et al (2012) Non-contrast T1-mapping detects acute myocardial edema with high diagnostic accuracy: a comparison to T2-weighted cardiovascular magnetic resonance. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-14-42 Google Scholar
  6. 6.
    Thavendiranathan P, Walls M, Giri S et al (2012) Improved detection of myocardial involvement in acute inflammatory cardiomyopathies using T2 mapping. Circ Cardiovasc Imaging. doi: 10.1161/CIRCIMAGING.111.967836 Google Scholar
  7. 7.
    Giri S, Chung Y-C, Merchant A et al (2009) T2 quantification for improved detection of myocardial edema. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-11-56 PubMedPubMedCentralGoogle Scholar
  8. 8.
    Hamlin SA, Henry TS, Little BP et al (2014) Mapping the future of cardiac MR imaging: case-based review of T1 and T2 mapping techniques. Radiographics. doi: 10.1148/rg.346140030 PubMedGoogle Scholar
  9. 9.
    Baeßler B, Schaarschmidt F, Stehning C et al (2015) A systematic evaluation of three different cardiac T2-mapping sequences at 1.5 and 3T in healthy volunteers. Eur J Radiol. doi: 10.1016/j.ejrad.2015.08.002 Google Scholar
  10. 10.
    Wassmuth R, Prothmann M, Utz W et al (2013) Variability and homogeneity of cardiovascular magnetic resonance myocardial T2-mapping in volunteers compared to patients with edema. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-15-27 Google Scholar
  11. 11.
    Radunski UK, Bohnen S, Lund G et al (2015) T1 and T2 mapping CMR to quantify focal myocardial injury in patients with myocarditis. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-17-S1-O90 Google Scholar
  12. 12.
    Butler CR, Savu A, Bakal JA et al (2015) Correlation of cardiovascular MRI findings and endomyocardial biopsy results in patients undergoing screening for heart transplant rejection. J Heart Lung Transplant. doi: 10.1016/j.healun.2014.12.020 Google Scholar
  13. 13.
    Baeßler B, Schaarschmidt F, Dick A et al (2015) Mapping tissue inhomogeneity in acute myocarditis: a novel analytical approach to quantitative myocardial edema imaging by T2-mapping. J Cardiovasc Magn Reson. doi: 10.1186/s12968-015-0217-y Google Scholar
  14. 14.
    Caforio ALP, Pankuweit S, Arbustini E et al (2013) Current state of knowledge on aetiology, diagnosis, management, and therapy of myocarditis: a position statement of the European Society of Cardiology Working Group on Myocardial and Pericardial Diseases. Eur Heart J. doi: 10.1093/eurheartj/eht210 Google Scholar
  15. 15.
    Simonetti OP, Finn JP, White RD et al (1996) “Black blood” T2-weighted inversion-recovery MR imaging of the heart. Radiology. doi: 10.1148/radiology.199.1.8633172 PubMedGoogle Scholar
  16. 16.
    Friedrich MGM, Strohm OO, Schulz-Menger JJ et al (1998) Contrast media-enhanced magnetic resonance imaging visualizes myocardial changes in the course of viral myocarditis. Circulation. doi: 10.1161/01.CIR.97.18.1802 PubMedGoogle Scholar
  17. 17.
    Kramer CM, Barkhausen J, Flamm SD et al (2008) Standardized cardiovascular magnetic resonance imaging (CMR) protocols, society for cardiovascular magnetic resonance: board of trustees task force on standardized protocols. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-10-35 Google Scholar
  18. 18.
    Baeßler B, Schaarschmidt F, Stehning C et al (2015) Cardiac T2-mapping using a fast gradient echo spin echo sequence - first in vitro and in vivo experience. J Cardiovasc Magn Reson. doi: 10.1186/s12968-015-0177-2 Google Scholar
  19. 19.
    Abdel-Aty H, Boyé P, Zagrosek A et al (2005) Diagnostic performance of cardiovascular magnetic resonance in patients with suspected acute myocarditis: comparison of different approaches. J Am Coll Cardiol. doi: 10.1016/j.jacc.2004.11.069 PubMedGoogle Scholar
  20. 20.
    Friedrich MG, Marcotte F (2013) Cardiac magnetic resonance assessment of myocarditis. Circ Cardiovasc Imaging. doi: 10.1161/CIRCIMAGING.113.000416 Google Scholar
  21. 21.
    Cerqueira MD, Verani MS, Schwaiger M et al (1994) Safety profile of adenosine stress perfusion imaging: results from the Adenoscan Multicenter Trial Registry. J Am Coll Cardiol. doi: 10.1016/0735-1097(94)90424-3 PubMedGoogle Scholar
  22. 22.
    R Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Available via http://www.R-project.org/. Accessed 08 March 2017
  23. 23.
    Wickham H, Chang W (2016) ggplot2: an implementation of the Grammar of Graphics. Available via http://ggplot2.tidyverse.org. Accessed 08 March 2017.
  24. 24.
    Grosjean P, Ibanez F, Etienne M (2002) Pastecs: package for analysis of space-time ecological series. Available via http://www.sciviews.org/pastecs. Accessed 08 March 2017
  25. 25.
    Maindonald JH, Braun WJ (2015) DAAG: data analysis and graphics data and functions. Available via http://www.stats.uwo.ca/DAAG. Accessed 08 March 2017
  26. 26.
    Liaw A, Wiener M, Breiman L, Cutler A (2015) randomForest: Breiman and Cutler's random forests for classification and regression. Available via https://www.stat.berkeley.edu/~breiman/RandomForests/. Accessed 08 March 2017
  27. 27.
    Therneau T, Atkinson B, Ripley B (2015) Recursive partitioning and regression trees. Available via http://www.R-project.org/. Accessed 08 March 2017
  28. 28.
    Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees, 1st edn. Chapman and Hall/CRC, BelmontGoogle Scholar
  29. 29.
    Sing T, Sander O, Beerenwinkel N, Lengauer T (2015) ROCR: visualizing the performance of scoring classifiers. Available via http://rocr.bioinf.mpi-sb.mpg.de/. Accessed 08 March 2017
  30. 30.
    Gutberlet M, Lücke C, Krieghoff C et al (2013) [MRI for myocarditis]. Radiologe. doi: 10.1007/s00117-012-2385-1 Google Scholar
  31. 31.
    Radunski UK, Lund GK, Stehning C et al (2014) CMR in patients with severe myocarditis: diagnostic value of quantitative tissue markers including extracellular volume imaging. J Am Coll Cardiol Img. doi: 10.1016/j.jcmg.2014.02.005 Google Scholar
  32. 32.
    Lurz P, Luecke C, Eitel I et al (2016) Comprehensive cardiac magnetic resonance imaging in patients with suspected myocarditis: the MyoRacer-Trial. J Am Coll Cardiol. doi: 10.1016/j.jacc.2016.02.013 Google Scholar
  33. 33.
    Moon JC, Messroghli DR, Kellman P et al (2013) Myocardial T1 mapping and extracellular volume quantification: a Society for Cardiovascular Magnetic Resonance (SCMR) and CMR Working Group of the European Society of Cardiology consensus statement. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-15-92 Google Scholar
  34. 34.
    Hinojar R, Foote L, Arroyo Ucar E et al (2015) Native T1 in discrimination of acute and convalescent stages in patients with clinical diagnosis of myocarditis: a proposed diagnostic algorithm using CMR. J Am Coll Cardiol Img. doi: 10.1016/j.jcmg.2014.07.016 Google Scholar
  35. 35.
    Ferreira VM, Piechnik SK, Dall'Armellina E et al (2013) T1 mapping for the diagnosis of acute myocarditis using CMR: comparison to T2-weighted and late gadolinium enhanced imaging. J Am Coll Cardiol Img. doi: 10.1016/j.jcmg.2013.03.008 Google Scholar
  36. 36.
    Luetkens JA, Doerner J, Thomas DK et al (2014) Acute myocarditis: multiparametric cardiac MR imaging. Radiology. doi: 10.1148/radiol.14132540 PubMedGoogle Scholar
  37. 37.
    Ferreira VM, Piechnik SK, Dall'Armellina E et al (2014) Native T1-mapping detects the location, extent and patterns of acute myocarditis without the need for gadolinium contrast agents. J Cardiovasc Magn Reson. doi: 10.1186/1532-429X-16-36 Google Scholar
  38. 38.
    Luetkens JA, Homsi R, Sprinkart AM et al (2015) Incremental value of quantitative CMR including parametric mapping for the diagnosis of acute myocarditis. Eur Heart J Cardiovasc Imaging. doi: 10.1093/ehjci/jev246 PubMedPubMedCentralGoogle Scholar
  39. 39.
    Baeßler B, Schaarschmidt F, Dick A et al (2016) Diagnostic implications of magnetic resonance feature tracking derived myocardial strain parameters in acute myocarditis. Eur J Radiol. doi: 10.1016/j.ejrad.2015.11.023 PubMedGoogle Scholar
  40. 40.
    Cooper LT, Baughman KL, Feldman AM et al (2007) The role of endomyocardial biopsy in the management of cardiovascular disease: a scientific statement from the American Heart Association, the American College of Cardiology, and the European Society of Cardiology Endorsed by the Heart Failure Society of America and the Heart Failure Association of the European Society of Cardiology. Eur Heart J. doi: 10.1093/eurheartj/ehm456 PubMedGoogle Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  1. 1.Department of RadiologyUniversity Hospital of CologneCologneGermany
  2. 2.Institute of Biostatistics, Faculty of Natural SciencesLeibniz Universität HannoverHannoverGermany
  3. 3.Philips ResearchHamburgGermany
  4. 4.Philips, Healthcare GermanyHamburgGermany
  5. 5.Department III of Internal Medicine, Heart CentreUniversity Hospital of CologneCologneGermany

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