Skip to main content
Top
Published in: Journal of Cardiovascular Magnetic Resonance 1/2015

Open Access 01-12-2015 | Research

Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours

Published in: Journal of Cardiovascular Magnetic Resonance | Issue 1/2015

Login to get access

Abstract

Background

High reproducibility of LV mass and volume measurement from cine cardiovascular magnetic resonance (CMR) has been shown within single centers. However, the extent to which contours may vary from center to center, due to different training protocols, is unknown. We aimed to quantify sources of variation between many centers, and provide a multi-center consensus ground truth dataset for benchmarking automated processing tools and facilitating training for new readers in CMR analysis.

Methods

Seven independent expert readers, representing seven experienced CMR core laboratories, analyzed fifteen cine CMR data sets in accordance with their standard operating protocols and SCMR guidelines. Consensus contours were generated for each image according to a statistical optimization scheme that maximized contour placement agreement between readers.

Results

Reader-consensus agreement was better than inter-reader agreement (end-diastolic volume 14.7 ml vs 15.2–28.4 ml; end-systolic volume 13.2 ml vs 14.0–21.5 ml; LV mass 17.5 g vs 20.2–34.5 g; ejection fraction 4.2 % vs 4.6–7.5 %). Compared with consensus contours, readers were very consistent (small variability across cases within each reader), but bias varied between readers due to differences in contouring protocols at each center. Although larger contour differences were found at the apex and base, the main effect on volume was due to small but consistent differences in the position of the contours in all regions of the LV.

Conclusions

A multi-center consensus dataset was established for the purposes of benchmarking and training. Achieving consensus on contour drawing protocol between centers before analysis, or bias correction after analysis, is required when collating multi-center results.
Literature
1.
go back to reference Bluemke DA, Kronmal RA, Lima JA, Liu K, Olson J, Burke GL, et al. The relationship of left ventricular mass and geometry to incident cardiovascular events: the MESA (Multi-Ethnic Study of Atherosclerosis) study. J Am Coll Cardiol. 2008;52:2148–55.PubMedCentralPubMedCrossRef Bluemke DA, Kronmal RA, Lima JA, Liu K, Olson J, Burke GL, et al. The relationship of left ventricular mass and geometry to incident cardiovascular events: the MESA (Multi-Ethnic Study of Atherosclerosis) study. J Am Coll Cardiol. 2008;52:2148–55.PubMedCentralPubMedCrossRef
2.
go back to reference Gjesdal O, Bluemke DA, Lima JA. Cardiac remodeling at the population level—risk factors, screening, and outcomes. Nat Rev Cardiol. 2011;8:673–85.PubMedCrossRef Gjesdal O, Bluemke DA, Lima JA. Cardiac remodeling at the population level—risk factors, screening, and outcomes. Nat Rev Cardiol. 2011;8:673–85.PubMedCrossRef
3.
go back to reference Grothues F, Smith GC, Moon JC, Bellenger NG, Collins P, Klein HU, et al. Comparison of interstudy reproducibility of cardiovascular magnetic resonance with two-dimensional echocardiography in normal subjects and in patients with heart failure or left ventricular hypertrophy. Am J Cardiol. 2002;90:29–34.PubMedCrossRef Grothues F, Smith GC, Moon JC, Bellenger NG, Collins P, Klein HU, et al. Comparison of interstudy reproducibility of cardiovascular magnetic resonance with two-dimensional echocardiography in normal subjects and in patients with heart failure or left ventricular hypertrophy. Am J Cardiol. 2002;90:29–34.PubMedCrossRef
4.
go back to reference Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156:871–81.PubMedCrossRef Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156:871–81.PubMedCrossRef
5.
go back to reference Petersen SE, Matthews PM, Bamberg F, Bluemke DA, Francis JM, Friedrich MG, et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank—rationale, challenges and approaches. J Cardiovasc Magn Reson. 2013;15:46.PubMedCentralPubMedCrossRef Petersen SE, Matthews PM, Bamberg F, Bluemke DA, Francis JM, Friedrich MG, et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank—rationale, challenges and approaches. J Cardiovasc Magn Reson. 2013;15:46.PubMedCentralPubMedCrossRef
6.
go back to reference Karamitsos TD, Hudsmith LE, Selvanayagam JB, Neubauer S, Francis JM. Operator induced variability in left ventricular measurements with cardiovascular magnetic resonance is improved after training. J Cardiovasc Magn Reson. 2007;9:777–83.PubMedCrossRef Karamitsos TD, Hudsmith LE, Selvanayagam JB, Neubauer S, Francis JM. Operator induced variability in left ventricular measurements with cardiovascular magnetic resonance is improved after training. J Cardiovasc Magn Reson. 2007;9:777–83.PubMedCrossRef
7.
go back to reference Pattynama PM, De Roos A, Van der Wall EE, Van Voorthuisen AE. Evaluation of cardiac function with magnetic resonance imaging. Am Heart J. 1994;128:595–607.PubMedCrossRef Pattynama PM, De Roos A, Van der Wall EE, Van Voorthuisen AE. Evaluation of cardiac function with magnetic resonance imaging. Am Heart J. 1994;128:595–607.PubMedCrossRef
8.
go back to reference Bottini PB, Carr AA, Prisant LM, Flickinger FW, Allison JD, Gottdiener JS. Magnetic resonance imaging compared to echocardiography to assess left ventricular mass in the hypertensive patient. Am J Hypertens. 1995;8:221–8.PubMedCrossRef Bottini PB, Carr AA, Prisant LM, Flickinger FW, Allison JD, Gottdiener JS. Magnetic resonance imaging compared to echocardiography to assess left ventricular mass in the hypertensive patient. Am J Hypertens. 1995;8:221–8.PubMedCrossRef
9.
go back to reference Suinesiaputra A, Cowan BR, Al-Agamy AO, AlAttar MA, Ayache N, Fahmy AS, et al. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Medical Image Analysis. 2014;18:50–62.PubMedCrossRef Suinesiaputra A, Cowan BR, Al-Agamy AO, AlAttar MA, Ayache N, Fahmy AS, et al. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Medical Image Analysis. 2014;18:50–62.PubMedCrossRef
10.
go back to reference Medrano-Gracia P, Cowan BR, Ambale-Venkatesh B, Bluemke DA, Eng J, Finn JP, et al. Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. J Cardiovasc Magn Reson. 2014;16:56.PubMedCentralPubMedCrossRef Medrano-Gracia P, Cowan BR, Ambale-Venkatesh B, Bluemke DA, Eng J, Finn JP, et al. Left ventricular shape variation in asymptomatic populations: the multi-ethnic study of atherosclerosis. J Cardiovasc Magn Reson. 2014;16:56.PubMedCentralPubMedCrossRef
11.
go back to reference Schulz-Menger J, Bluemke DA, Bremerich J, Flamm SD, Fogel MA, Friedrich MG, et al. Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing. J Cardiovasc Magn Reson. 2013;15:35.PubMedCentralPubMedCrossRef Schulz-Menger J, Bluemke DA, Bremerich J, Flamm SD, Fogel MA, Friedrich MG, et al. Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing. J Cardiovasc Magn Reson. 2013;15:35.PubMedCentralPubMedCrossRef
12.
go back to reference Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004;23:903–21.PubMedCentralPubMedCrossRef Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004;23:903–21.PubMedCentralPubMedCrossRef
13.
go back to reference Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol. 1977;39:1–38. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol. 1977;39:1–38.
14.
go back to reference Medrano-Gracia P, Cowan BR, Bluemke DA, Finn JP, Kadish AH, Lee DC, et al. Atlas-based analysis of cardiac shape and function: correction of regional shape bias due to imaging protocol for population studies. J Cardiovasc Magn Reson. 2013;15:80.PubMedCentralPubMedCrossRef Medrano-Gracia P, Cowan BR, Bluemke DA, Finn JP, Kadish AH, Lee DC, et al. Atlas-based analysis of cardiac shape and function: correction of regional shape bias due to imaging protocol for population studies. J Cardiovasc Magn Reson. 2013;15:80.PubMedCentralPubMedCrossRef
15.
go back to reference Abbey CK, Samuelson FW, Gallas BD. Statistical power considerations for a utility endpoint in observer performance studies. Acad Radiol. 2013;20:798–806.PubMedCrossRef Abbey CK, Samuelson FW, Gallas BD. Statistical power considerations for a utility endpoint in observer performance studies. Acad Radiol. 2013;20:798–806.PubMedCrossRef
16.
go back to reference Voth M, Attenberger UI, Luckscheiter A, Haneder S, Henzler T, Schoenberg SO, et al. “Number needed to read”—how to facilitate clinical trials in MR-angiography. Eur Radiol. 2011;21:1034–42.PubMedCentralPubMedCrossRef Voth M, Attenberger UI, Luckscheiter A, Haneder S, Henzler T, Schoenberg SO, et al. “Number needed to read”—how to facilitate clinical trials in MR-angiography. Eur Radiol. 2011;21:1034–42.PubMedCentralPubMedCrossRef
17.
go back to reference Fonseca CG, Backhaus M, Bluemke DA, Britten RD, Chung JD, Cowan BR, et al. The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics. 2011;27:2288–95.PubMedCentralPubMedCrossRef Fonseca CG, Backhaus M, Bluemke DA, Britten RD, Chung JD, Cowan BR, et al. The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics. 2011;27:2288–95.PubMedCentralPubMedCrossRef
18.
go back to reference Papavassiliu T, Kuhl HP, Schroder M, Suselbeck T, Bondarenko O, Bohm CK, et al. Effect of endocardial trabeculae on left ventricular measurements and measurement reproducibility at cardiovascular MR imaging. Radiology. 2005;236:57–64.PubMedCrossRef Papavassiliu T, Kuhl HP, Schroder M, Suselbeck T, Bondarenko O, Bohm CK, et al. Effect of endocardial trabeculae on left ventricular measurements and measurement reproducibility at cardiovascular MR imaging. Radiology. 2005;236:57–64.PubMedCrossRef
19.
go back to reference Vogel-Claussen J, Finn JP, Gomes AS, Hundley GW, Jerosch-Herold M, Pearson G, et al. Left ventricular papillary muscle mass: relationship to left ventricular mass and volumes by magnetic resonance imaging. J Comput Assist Tomogr. 2006;30:426–32.PubMedCrossRef Vogel-Claussen J, Finn JP, Gomes AS, Hundley GW, Jerosch-Herold M, Pearson G, et al. Left ventricular papillary muscle mass: relationship to left ventricular mass and volumes by magnetic resonance imaging. J Comput Assist Tomogr. 2006;30:426–32.PubMedCrossRef
Metadata
Title
Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours
Publication date
01-12-2015
Published in
Journal of Cardiovascular Magnetic Resonance / Issue 1/2015
Electronic ISSN: 1532-429X
DOI
https://doi.org/10.1186/s12968-015-0170-9

Other articles of this Issue 1/2015

Journal of Cardiovascular Magnetic Resonance 1/2015 Go to the issue