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Accuracy in Quantification of Coronary Calcification with CT

Rationale and Objectives

Coronary artery calcium is a sensitive risk predictor of cardiac events. However, measurement of calcium foci is affected by partial-volume effects, which ultimately have an effect on accuracy and reproducibility of calcium scores. In this study, we describe the accuracy of quantification of calcium foci of known size and density using cork-dog heart phantoms.

Materials and Methods

Five study phantoms were constructed from cork chests and dog hearts containing 135 calcium hydroxyapatite (CaHA) foci of known volume, mass, and concentration located in the coronary arteries or the myocardium. Hearts were separated into two groups: (1) three hearts containing large, high-density foci and (2) two hearts containing small, low-density foci. The phantoms were scanned using a standard coronary artery calcium (CAC) protocol and the volume and mean intensity of foci were measured.

Results

In group 1, the total volume of 87 CaHA foci measured was 4284 and 3779 mm 3 with electron beam computed tomography (EBCT); multidetector computed tomography (MDCT), respectively ( P < .001). Both were significantly larger than the true volume (2713.9 mm 3 , P < .001). In Group 2, the total volume of 57 CaHA foci measured was 592.6 and 702.9 mm 3 with EBT and MDCT, respectively ( P < .001). Both were significantly smaller than the true volume (1733.2 mm 3 , P < .001). We found that EBCT values for volume were approximately generally higher than MDCT values, but strongly correlated ( r = 0.95, P < .0001). Agatston scores were found to be nearly equivalent between EBCT and MDCT and were similarly strongly correlated ( r = 0.97, P < .0001).

Conclusions

Computed tomography images overestimate the volume of large, dense CaHA foci while underestimating the volume of smaller (<6.6 mm 3 ), less dense foci. This may have significant implications on CAC scoring and volume measurement. EBCT overestimated calcium more than MDCT, most likely from increased image noise.

Coronary artery calcium (CAC) measurement via electron-beam computed tomography (EBCT) and multidetector-row computed tomography (MDCT) is clinically useful as a noninvasive, non–exercise-dependent predictor of risk of cardiac events . CAC is consistently associated with atherosclerotic burden, independent of Framingham risk factors and information obtained via catheterization and angiography . Recent studies show that CAC measurement is a stronger predictor of coronary vascular disease (CVD) occurrence than carotid intima-media thickness . Additionally, worsening progression of CAC scores has been correlated with increased risk of first myocardial infarction .

Despite improvements in imaging technology, however, the accuracy of calcium foci quantification remains affected by constraints inherent to three-dimensional imaging modalities. Partial-volume effects (PVE) are phenomena that affect three-dimensional reconstructed image quality because of limits in spatial resolution and image sampling . PVE are better described in relation to modalities such as positron emission tomography and magnetic resonance imaging, which feature lower spatial resolution. However, PVE occur despite the high spatial resolution offered by computed tomography (CT) as an innate effect of pixel (and thus, voxel) edges that may not accurately match the contours of actual foci. As a result, in pixels partially occupied by calcium, intensity is not lost, but is instead dispersed across the whole area of a given pixel ( Fig 1 ), producing a lower intensity for the entire unit of volume. This effect can adversely affect quantification of CAC. For example, in the widely used Agatston calcium scoring method, points are assigned to calcified regions exceeding an arbitrary threshold for intensity measured in Hounsfield units (HU) . Thus, because of partial-volume effects, scores theoretically underestimate regions of small size and density because of decreased voxel intensity, while conversely overestimating regions of larger size and density. These phenomena can contribute to significant interscan variability as recently noted by Rutten et al, who found that the starting position of a given scan significantly alters the degree to which PVE affects an individual scan .

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Figure 1

Illustration of volume averaging effects on a circular mass of homogeneous density. When sampled in a 3 × 3 pixel grid, peripheral pixels demonstrate markedly reduced signal intensity.

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Methods

Phantom Construction

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Figure 2, Cork chest simulation phantoms. Image on the right shows a labeled transverse image of the canine heart at the ventricular level. RV, right ventricle; LV, left ventricle; LAD, left anterior descending coronary artery; PDA, posterior descending artery; W, wall; C, calibration phantom.

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CT Study Protocol

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Table 1

EBCT and MDCT Scanning Parameters

Parameter EBCT Imatron C-300 MDCT Lightspeed VCT64 Peak voltage (kVp) 130 140 Scanning time EBCT 100 ms Rotation time, MDCT 350 ms Temporal resolution 227ms Tube current (mA) 630 430 (220–670) ∗ Tube current-time product 63 mAs Varies ∗ Detector configuration 1 × 3 mm 4 × 2.5 mm Section thickness (mm) 3 2.5 Table increment (mm) 3 10 Reconstruction kernel Sharp Standard

EBCT, electron beam computed tomography; MDCT, multidetector computed tomography.

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Measurements

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Statistical Analysis

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Results

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Table 2

Measured Volumes of Calcium Foci

Group 1 Group 2 Sum Volume (mm 3 ) Mean Volume (mm 3 ) Sum Volume (mm 3 ) Mean Volume (mm 3 ) Actual 2713.93 (28.93) 34.79 ± 25.52 1733.18 (15.41) 33.33 ± 34.90 MDCT 3779.11 (36.34) 48.45 ± 35.60 702.88 (3.63) 13.52 ± 23.80 EBCT 4284 (41.89) 54.92 ± 42.74 592.61 (1.61) 11.40 ± 23.64

EBCT, electron beam computed tomography; MDCT, multidetector computed tomography.

Sums are given with median in parentheses.

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Figure 3, (a) Correlation of volume measurements by EBCT and MDCT. The MDCT values, overall, are consistently lower by approximately 11%. (b) Correlation of Agatston calcium scorings using the same data set as (a) . EBCT, electron beam computed tomography; MDCT, multidetector computed tomography.

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Discussion

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Conclusions

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References

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