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Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data

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Abstract

Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a per-patient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (±15.80)%, 93.54 (±1.98)%, and 85.27 (±14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (±7.78)%, 96.57 (±4.90)%, and 73.58 (±8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.

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Notes

  1. See "Methods" Section for an exact definition of zones and sections.

  2. Train CAR classifier using all patient data except for one data set and test on the left-out data set. Repeat for all patients and report the averaged performance.

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Acknowledgments

This work was supported in part by the Biomedical discovery Training Program of the W.M. Keck Center for Interdisciplinary Bioscience Training of the Gulf Coast Consortia (NIH Grant No. 1 T90 DA022885 and 1 R90 Da023418), and in part by NSF Grants IIS-0431144, and CNS-0521527.

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Authors and Affiliations

  1. Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, USA

    Gerd Brunner, Deepak R. Chittajallu & Uday Kurkure

  2. Computational Biomedicine Lab, Departments of Computer Science, Electronics and Computer Engineering, and Biomedical Engineering, University of Houston, Houston, TX, USA

    Ioannis A. Kakadiaris

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  1. Gerd Brunner
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  2. Deepak R. Chittajallu
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  3. Uday Kurkure
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  4. Ioannis A. Kakadiaris
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Correspondence to Gerd Brunner.

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Brunner, G., Chittajallu, D.R., Kurkure, U. et al. Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data. Int J Cardiovasc Imaging 26, 829–838 (2010). https://doi.org/10.1007/s10554-010-9608-1

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  • DOI: https://doi.org/10.1007/s10554-010-9608-1

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