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Updated: Jul 10, 2026

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

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Published on: August 28, 2018

Automated pericardial fat quantification in CT data.

Alok N Bandekar¹, Morteza Naghavi, Ioannis A Kakadiaris

¹Computational Biomedicine Lab, Department of Computer Science, University of Houston, TX 72204, USA. abandekar@uh.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

Pericardial fat, a cardiovascular risk factor, can now be automatically quantified. This new method overcomes limitations of manual analysis, improving accuracy in assessing fat burden from CT scans.

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Area of Science:

Cardiovascular Imaging and Diagnostics
Medical Image Analysis
Quantitative Imaging

Background:

Emerging evidence links pericardial fat to significant cardiovascular risk.
Computed tomography (CT) scans for coronary calcium scoring routinely capture pericardial fat.
Current analysis of CT images ignores pericardial fat due to a lack of automated quantification tools.

Purpose of the Study:

To develop and present a novel method for automatic quantification and classification of pericardial fat burden.
To address the limitations of manual region-of-interest outlining and fixed fat attenuation thresholds in previous studies.
To overcome inter-observer and inter-scan variability inherent in manual pericardial fat assessment.

Main Methods:

Development of an automated algorithm for pericardial fat quantification from CT images.
Utilized CT imaging data from a cohort of 23 subjects for method evaluation.
Focused on automatic burden quantification and classification of pericardial fat.

Main Results:

The developed method demonstrated highly encouraging performance in quantifying pericardial fat burden.
Results indicate successful automatic classification of pericardial fat.
Validation using data from 23 subjects supports the efficacy of the automated approach.

Conclusions:

The presented method offers a reliable solution for automatic pericardial fat quantification.
This automated approach has the potential to improve cardiovascular risk assessment by incorporating pericardial fat analysis.
Future research can leverage this tool for more accurate and consistent evaluation of pericardial fat in clinical practice.