Calcium screening with EBCT is a highly sensitive and moderately specific test to predict stenotic disease. Exclusion of coronary calcium defines a substantial subgroup of patients, albeit symptomatic, with a very low probability of significant stenoses.

Background-Coronary calcification measured by fast computed tomography techniques is a surrogate marker of coronary atherosclerotic plaque burden. In a cohort study, we prospectively investigated whether lipid-lowering therapy with a cholesterol synthesis enzyme inhibitor reduces the progression of coronary calcification. Methods and Results-In 66 patients with coronary calcifications in electron beam tomography (EBT), LDL cholesterol Ͼ130 mg/dL, and no lipid-lowering treatment, the EBT scan was repeated after a mean interval of 14 months and treatment with cerivastatin was initiated (0.3 mg/d). After 12 months of treatment, a third EBT scan was performed. Coronary calcifications were quantified using a volumetric score. Cerivastatin therapy lowered the mean LDL cholesterol level from 164Ϯ30 to 107Ϯ21 mg/dL. The median calcified volume was 155 mm 3 (range, 15 to 1849) at baseline, 201 mm 3 (19 to 2486) after 14 months without treatment, and 203 mm 3 (15 to 2569) after 12 months of cerivastatin treatment. The median annualized absolute increase in coronary calcium was 25 mm 3 during the untreated versus 11 mm 3 during the treatment period (Pϭ0.01). The median annual relative increase in coronary calcium was 25% during the untreated versus 8.8% during the treatment period (PϽ0.0001). In 32 patients with an LDL cholesterol level Ͻ100 mg/dL under treatment, the median relative change was 27% during the untreated versus Ϫ3.4% during the treatment period (Pϭ0.0001). Conclusions-Treatment with the cholesterol synthesis enzyme inhibitor cerivastatin significantly reduces coronary calcium progression in patients with LDL cholesterol Ͼ130 mg/dL.

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ECG-triggered single-slice conventional CT had the best agreement with electron beam CT calcium scores.

In this investigation, we describe a quantitative technique to measure coronary motion, which can be correlated with cardiac image quality using multislice computed tomography (MSCT) scanners. MSCT scanners, with subsecond scanning, thin-slice imaging (sub-millimeter) and volume scanning capabilities have paved the way for new clinical applications like noninvasive cardiac imaging. ECG-gated spiral CT using MSCT scanners has made it possible to scan the entire heart in a single breath-hold. The continuous data acquisition makes it possible for multiple phases to be reconstructed from a cardiac cycle. We measure the position and three-dimensional velocities of well-known landmarks along the proximal, mid, and distal regions of the major coronary arteries [left main (LM), left anterior descending (LAD), right coronary artery (RCA), and left circumflex (LCX)] during the cardiac cycle. A dynamic model (called the "delay algorithm") is described which enables us to capture the same physiological phase or "state" of the anatomy during the cardiac cycle as the instantaneous heart rate varies during the spiral scan. The coronary arteries are reconstructed from data obtained during different physiological cardiac phases and we correlate image quality of different parts of the coronary anatomy with phases at which minimum velocities occur. The motion characteristics varied depending on the artery, with the highest motion being observed for RCA. The phases with the lowest mean velocities provided the best visualization. Though more than one phase of relative minimum velocity was observed for each artery, the most consistent image quality was observed during mid-diastole ("diastasis") of the cardiac cycle and was judged to be superior to other reconstructed phases in 92% of the cases. In the process, we also investigated correlation between cardiac arterial states and other measures of motion, such as the left ventricular volume during a cardiac cycle, which earlier has been demonstrated as an example of how anatomic-specific information can be used in a knowledge-based cardiac CT algorithm. Using these estimates in characterizing cardiac motion also provides realistic simulation models for higher heart rates and also in optimizing volume reconstructions for individual segments of the cardiac anatomy.