Coronary calcium quantification using contrast-enhanced dual-energy computed tomography scans.

The purpose of this study is to evaluate a direct measure of calcium burden by using dual-energy computed tomography (DECT) during contrast-enhanced coronary imaging, potentially eliminating the need for an extra noncontrast X-ray acquisition. The ambiguity of separation of calcium from contrast material on contrast-enhanced images was solved by using virtual noncontrast images obtained by DECT. A new threshold CT number was required to detect the calcium carrying potential risk for adverse coronary events on virtual noncontrast images. Two methods were investigated to determine the 130 HU threshold for DECT scoring. An in vitro anthropomorphic phantom with 29 excised patient calcium plaques inserted was used for both a linear and a logistic regression analysis. An IRB approved in vivo prospective study of six patients was also performed to be used for logistic regression analysis. The threshold found by logistic regression model to define the calcium burden on virtual noncontrast images detects the calcium carrying potential risk for adverse coronary events correctly (2.45% error rate). DECT calcium mass and volume scores obtained by using the threshold correlates with both conventional Agatston and volume scores (r = 0.98, p < 0.001). A conventional CT cardiac exam requires two scans, including a noncontrast scan for calcium quantification and a contrast-enhanced scan for coronary angiography. With the ability to quantify calcium on DECT contrast-enhanced images, a DECT cardiac exam could be accomplished with one contrast-enhanced scan for both calcium quantification and coronary angiography. 

Non-calcified coronary atherosclerotic plaque characterization by dual energy computed tomography.

Coronary heart disease (CHD) is the most prevalent cause of death worldwide. Atherosclerosis which is the condition of plaque buildup on the inside of the coronary artery wall is the main cause of CHD. Rupture of unstable atherosclerotic coronary plaque is known to be the cause of acute coronary syndrome. Vulnerability of atherosclerotic plaque has been related to a large lipid core covered by a fibrous cap. Non-invasive assessment of plaque characterization is necessary due to prognostic importance of early stage identification. The purpose of this study is to use the additional attenuation data provided by dual energy computed tomography (DECT) for plaque characterization. We propose to train supervised learners on pixel values recorded from DECT monochromatic X-ray and material basis pairs images, for more precise classification of fibrous and lipid plaques. The interaction of the pixel values from different image types is taken into consideration, as single pixel value might not be informative enough to separate fibrous from lipid. Organic phantom plaques scanned in a fabricated beating heart phantom were used as ground truth to train the learners. Our results show that support vector machines, artificial neural networks and random forests provide accurate results both on phantom and patient data.