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Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool

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Abstract

The purpose of this study was to investigate a 3D coronary artery segmentation algorithm using 16-row MDCT data sets. Fifty patients underwent cardiac CT (Sensation 16, Siemens) and coronary angiography. Automatic and manual detection of coronary artery stenosis was performed. A 3D coronary artery segmentation algorithm (Fraunhofer Institute for Computer Graphics, Darmstadt) was used for automatic evaluation. All significant stenoses (>50%) in vessels >1.5 mm in diameter were protocoled. Each detection tool was used by one reader who was blinded to the results of the other detection method and the results of coronary angiography. Sensitivity and specificity were determined for automatic and manual detection as well as was the time for both CT-based evaluation methods. The overall sensitivity and specificity of the automatic and manual approach were 93.1 vs. 95.83% and 86.1 vs. 81.9%. The time required for automatic evaluation was significantly shorter than with the manual approach, i.e., 246.04±43.17 s for the automatic approach and 526.88±45.71 s for the manual approach (P<0.0001). In 94% of the coronary artery branches, automatic detection required less time than the manual approach. Automatic coronary vessel evaluation is feasible. It reduces the time required for cardiac CT evaluation with similar sensitivity and specificity as well as facilitates the evaluation of MDCT coronary angiography in a standardized fashion.

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Abbreviations

CABG:

coronary artery bypass graft

ACVB:

aortocoronary venous bypass

LITA:

left internal thoracic artery

LAD:

left anterior descending artery

RCA:

right coronary artery

LCX:

left circumflex artery

MIP:

thin slap maximum intensity projection

MPR:

multiplanar reformation

VRT:

volume-rendering reformation

FOV:

field of view

TECAB:

totally endoscopic coronary artery bypass

MDCT:

multidetector computed tomography

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Authors and Affiliations

  1. Institute for Diagnostic and Interventional Radiology, Johann Wolfgang Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany

    M. Fawad Khan, Jessen Gurung, Adel Maataoui, Boris Brehmer, Christopher Herzog & Thomas J. Vogl

  2. Fraunhofer Institute for Computer Graphics, Darmstadt, Germany

    Stefan Wesarg

  3. Department for Thoracic and Cardiovascular Surgery, Johann Wolfgang Goethe University, Frankfurt/Main, Germany

    Selami Dogan

  4. Institute for Epidemilogy and Medical Statistics, Johann Wolfgang Goethe University, Frankfurt/Main, Germany

    Hanns Ackermann

  5. Department of Internal Medicine III-Cardiology, Johann Wolfgang Goethe University, Frankfurt/Main, Germany

    Birgit Aßmus

Authors
  1. M. Fawad Khan
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  2. Stefan Wesarg
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  3. Jessen Gurung
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  4. Selami Dogan
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  5. Adel Maataoui
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  6. Boris Brehmer
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  7. Christopher Herzog
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  8. Hanns Ackermann
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  9. Birgit Aßmus
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  10. Thomas J. Vogl
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Correspondence to M. Fawad Khan.

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Khan, M.F., Wesarg, S., Gurung, J. et al. Facilitating coronary artery evaluation in MDCT using a 3D automatic vessel segmentation tool. Eur Radiol 16, 1789–1795 (2006). https://doi.org/10.1007/s00330-006-0159-8

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  • DOI: https://doi.org/10.1007/s00330-006-0159-8

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