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Tohoku J Exp Med ; 213(3): 249-59, 2007 Nov.
Article in English | MEDLINE | ID: mdl-17984622

ABSTRACT

Anomalous coronary artery (ACA) has either an unusual origin or different anatomical course and is associated with sudden cardiac death. The absence or nonspecific symptoms of ACA make its diagnosis difficult. Mostly, ACA is diagnosed coincidentally during invasive coronary angiogram (ICA). A conventional computed tomography (CT) cannot provide detailed images of coronary arteries of the moving heart, but 64-slice CT, with its short acquisition time, can provide detailed anatomy of coronary arteries non-invasively. In this study, we assessed the validity of contrast-enhanced 64-slice CT in the evaluation of ACA. ICA was performed in 7,574 patients for the diagnosis or evaluation of occlusive coronary artery disease and detected coronary anomalies in 56 patients (0.7%). We then performed 64-slice CT in 53 patients out of the 56 patients with demonstrated or suspected coronary anomaly, showing the origin and the course of the ACA along with stenosis, except for one patient who could not be evaluated due to image distortion artifacts. Contrast-enhanced 64-slice CT was also performed in 374 patients with vague signs and symptoms, detecting coronary anomalies in 7 patients (1.2%). Thus, in the total of 59 patients undergone 64-slice CT, we were able to visualize the entire abnormal coronary tree with a high diagnostic image quality. This is the first study to demonstrate the utility of 64-slice CT in a large series of ACA. Contrast-enhanced 64-slice CT is superior to ICA to identify the presence and course of ACA and should be the first line diagnostic tool in the evaluation of ACA.


Subject(s)
Coronary Vessel Anomalies/diagnosis , Coronary Vessel Anomalies/pathology , Coronary Vessels/pathology , Tomography, X-Ray Computed/methods , Aged , Contrast Media/pharmacology , Coronary Angiography/methods , Coronary Stenosis , Coronary Vessel Anomalies/diagnostic imaging , Female , Humans , Image Processing, Computer-Assisted , Male , Middle Aged , Radiographic Image Interpretation, Computer-Assisted , Risk Factors
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