Skip to main content
SpringerLink
Log in

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

  • Cardiac
  • Published:
European Radiology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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

References

  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

    PubMed  PubMed Central  Google Scholar 

  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. 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

    PubMed Central  Google Scholar 

  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. 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. 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. 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

    PubMed  PubMed Central  Google Scholar 

  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

    PubMed  Google Scholar 

  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. 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. 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. 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. 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. 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. 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

    PubMed  Google Scholar 

  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

    PubMed  Google Scholar 

  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. 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. 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

    PubMed  Google Scholar 

  20. Friedrich MG, Marcotte F (2013) Cardiac magnetic resonance assessment of myocarditis. Circ Cardiovasc Imaging. doi:10.1161/CIRCIMAGING.113.000416

    Google Scholar 

  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

    PubMed  Google Scholar 

  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. Wickham H, Chang W (2016) ggplot2: an implementation of the Grammar of Graphics. Available via http://ggplot2.tidyverse.org. Accessed 08 March 2017.

  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. 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. 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. Therneau T, Atkinson B, Ripley B (2015) Recursive partitioning and regression trees. Available via http://www.R-project.org/. Accessed 08 March 2017

  28. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees, 1st edn. Chapman and Hall/CRC, Belmont

    Google Scholar 

  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. Gutberlet M, Lücke C, Krieghoff C et al (2013) [MRI for myocarditis]. Radiologe. doi:10.1007/s00117-012-2385-1

    Google Scholar 

  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. 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. 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. 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. 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. Luetkens JA, Doerner J, Thomas DK et al (2014) Acute myocarditis: multiparametric cardiac MR imaging. Radiology. doi:10.1148/radiol.14132540

    PubMed  Google Scholar 

  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. 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

    PubMed  PubMed Central  Google Scholar 

  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

    PubMed  Google Scholar 

  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

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, University Hospital of Cologne, Kerpener Str. 62, D-50937, Cologne, Germany

    Bettina Baeßler, Melanie Treutlein, David Maintz & Alexander C. Bunck

  2. Institute of Biostatistics, Faculty of Natural Sciences, Leibniz Universität Hannover, Hannover, Germany

    Frank Schaarschmidt

  3. Philips Research, Hamburg, Germany

    Christian Stehning

  4. Philips, Healthcare Germany, Hamburg, Germany

    Bernhard Schnackenburg

  5. Department III of Internal Medicine, Heart Centre, University Hospital of Cologne, Cologne, Germany

    Guido Michels

Authors
  1. Bettina Baeßler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Schaarschmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Melanie Treutlein
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christian Stehning
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bernhard Schnackenburg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guido Michels
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David Maintz
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alexander C. Bunck
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bettina Baeßler.

Ethics declarations

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 199 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baeßler, B., Schaarschmidt, F., Treutlein, M. et al. Re-evaluation of a novel approach for quantitative myocardial oedema detection by analysing tissue inhomogeneity in acute myocarditis using T2-mapping. Eur Radiol 27, 5169–5178 (2017). https://doi.org/10.1007/s00330-017-4894-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-017-4894-9

Keywords

Search

Navigation