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Updated: Jul 17, 2026
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
An accurate and reproducible scheme for quantification of coronary artery calcification in CT scans
Jamshid Dehmeshki1, Xujiong Ye, Frank Wang
1Medicsight PLC, London, UK.
Insights
A novel 3D method using a modified expectation-maximisation algorithm improves accuracy and reproducibility in estimating coronary artery calcification (CAC) for monitoring coronary artery disease progression.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Research
- Quantitative Analysis
Background:
- Coronary artery disease (CAD) is a leading cause of mortality.
- Coronary artery calcification (CAC) is a key indicator of CAD.
- Existing CAC estimation methods lack accuracy and reproducibility.
Purpose of the Study:
- To develop and validate a new adaptive and stochastic 3D method for CAC estimation.
- To improve the accuracy and reproducibility of CAC monitoring in follow-up studies.
- To overcome limitations of traditional CAC assessment methods.
Main Methods:
- Proposed a novel adaptive and stochastic 3D method utilizing a modified expectation-maximisation (MEM) algorithm.
- Evaluated accuracy using a cardiac CT phantom with known calcium inserts under various scanning protocols.
- Assessed reproducibility by rescanning 35 patients after repositioning.
Main Results:
- The proposed MEM-based algorithm demonstrated reduced sensitivity to partial volume effects, motion, slice thickness, and low radiation dose.
- The method showed superior accuracy and reproducibility compared to the traditional Agatston-based method in phantom and patient studies.
Conclusions:
- The new 3D adaptive and stochastic method offers a more accurate and reproducible approach for CAC quantification.
- This technique holds potential for improved monitoring of coronary artery calcification progression in clinical practice.
Abstract:
The coronary artery disease is a major cause of deaths in the western world. One indicator for coronary artery disease (CAD) is coronary artery calcification (CAC). An accurate and reproducible scheme is desired to monitor the progression of patient's coronary calcification in follow-up studies. Traditional approaches for CAC estimation lack to provide accurate and reproducible results. In This work, a new adaptive and stochastic 3D method has been proposed by employing a modified expectation-maximisation (MEM) algorithm. It is less sensitive to partial volume effects, motion effects, slice thickness and low dose. Accuracy of the proposed method was measured by a cardiac CT stationary phantom containing 6 calcium inserts of predetermined size and density that were scanned 90 times using 15 different protocols based on slice thickness and radiation. Reproducibility was measured in 35 patients who were each scanned twice with the patient being repositioned before the second scan. Compared with the Agatston based method, it is shown that the proposed algorithm gives better results in terms of accuracy and reproducibility.
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