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Automated Quantification of Epicardial Adipose Tissue Using CT Angiography: Evaluation of a Prototype Software

  • Cardiac
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Abstract

Objectives

This study evaluated the performance of a novel automated software tool for epicardial fat volume (EFV) quantification compared to a standard manual technique at coronary CT angiography (cCTA).

Methods

cCTA data sets of 70 patients (58.6 ± 12.9 years, 33 men) were retrospectively analysed using two different post-processing software applications. Observer 1 performed a manual single-plane pericardial border definition and EFVM segmentation (manual approach). Two observers used a software program with fully automated 3D pericardial border definition and EFVA calculation (automated approach). EFV and time required for measuring EFV (including software processing time and manual optimization time) for each method were recorded. Intraobserver and interobserver reliability was assessed on the prototype software measurements. T test, Spearman’s rho, and Bland–Altman plots were used for statistical analysis.

Results

The final EFVA (with manual border optimization) was strongly correlated with the manual axial segmentation measurement (60.9 ± 33.2 mL vs. 65.8 ± 37.0 mL, rho = 0.970, P < 0.001). A mean of 3.9 ± 1.9 manual border edits were performed to optimize the automated process. The software prototype required significantly less time to perform the measurements (135.6 ± 24.6 s vs. 314.3 ± 76.3 s, P < 0.001) and showed high reliability (ICC > 0.9).

Conclusions

Automated EFVA quantification is an accurate and time-saving method for quantification of EFV compared to established manual axial segmentation methods.

Key Points

• Manual epicardial fat volume quantification correlates with risk factors but is time-consuming.

• The novel software prototype automates measurement of epicardial fat volume with good accuracy.

• This novel approach is less time-consuming and could be incorporated into clinical workflow.

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Abbreviations

CAD:

coronary artery disease

cCTA:

coronary computed tomography angiography

CTA:

computed tomography angiography

EAT:

epicardial adipose tissue

EFV:

epicardial fat volume

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Acknowledgements

Dr. Schoepf is a consultant for and receives research support from Bayer, Bracco, GE, Medrad, and Siemens. C. Canstein is a Siemens employee. The other authors have no conflict of interest to disclose.

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Authors and Affiliations

  1. Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower 25 Courtenay Drive, Charleston, SC, 29425-2260, USA

    James V. Spearman, Felix G. Meinel, U. Joseph Schoepf, Paul Apfaltrer, Justin R. Silverman, Aleksander W. Krazinski, Carlo Nicola De Cecco, Philip Costello & Lucas L. Geyer

  2. Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany

    Felix G. Meinel & Lucas L. Geyer

  3. Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA

    U. Joseph Schoepf

  4. Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Mannheim, Germany

    Paul Apfaltrer

  5. Siemens Medical Solutions USA, Inc., Malvern, PA, USA

    Christian Canstein

  6. Department of Radiological Sciences, Oncology and Pathology, University of Rome “Sapienza” – Polo Pontino, Latina, Italy

    Carlo Nicola De Cecco

Authors
  1. James V. Spearman
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  2. Felix G. Meinel
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  3. U. Joseph Schoepf
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  6. Aleksander W. Krazinski
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  7. Christian Canstein
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  8. Carlo Nicola De Cecco
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  9. Philip Costello
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  10. Lucas L. Geyer
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Correspondence to U. Joseph Schoepf.

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Spearman, J.V., Meinel, F.G., Schoepf, U.J. et al. Automated Quantification of Epicardial Adipose Tissue Using CT Angiography: Evaluation of a Prototype Software. Eur Radiol 24, 519–526 (2014). https://doi.org/10.1007/s00330-013-3052-2

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  • DOI: https://doi.org/10.1007/s00330-013-3052-2

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