BACKGROUND: Gated blood pool single photon emission computed tomography (SPECT) (GBPS) uses truly 3-dimensional (3D) data, requiring attention to appropriate reference systems and segmentation models for proper quantification. To date, optimal 3D reference models have not been evaluated. In this study several techniques for 3D GBPS were evaluated. METHODS AND RESULTS: Static and dynamic cardiac phantom evaluations were performed, and GBPS studies for 3 healthy subjects and 9 patients were processed by a variety of 3D analysis techniques to determine the optimum parameters for identification of abnormal segments when compared with coronary arteriography and left ventriculography. Left ventricular wall motion was quantified by calculation of regional ejection fraction (rEF) through use of count, volume, and cord length changes from end diastole to end systole. Three contractile models were evaluated: (1) fixed center of mass (COM), (2) floating COM, and (3) a modification of the method developed by Slager et al (J Am Coll Cardiol 1986;7:317-26), based on the motion of implanted endocardial markers. Eight, twelve, and eighteen 3D segments were analyzed by means of the 3 contractile models and correlated against coronary artery disease assessed by coronary arteriography. Single-head gamma-camera acquisition provided adequate counting statistics to reliably compute rEF for up to 18 left ventricular segments. Using count changes the overall results were able to identify myocardium supplied by diseased coronary arteries when compared with coronary arteriography. Cord length and, to a lesser degree, volume changes provided somewhat poorer sensitivities and specificities when compared with rEF computed from regional count changes, as compared with coronary arteriography. CONCLUSIONS: Three-dimensional quantitative GBPS appears to be a sensitive method for assessing wall motion defects due to coronary artery disease.
BACKGROUND: Gated blood pool single photon emission computed tomography (SPECT) (GBPS) uses truly 3-dimensional (3D) data, requiring attention to appropriate reference systems and segmentation models for proper quantification. To date, optimal 3D reference models have not been evaluated. In this study several techniques for 3D GBPS were evaluated. METHODS AND RESULTS: Static and dynamic cardiac phantom evaluations were performed, and GBPS studies for 3 healthy subjects and 9 patients were processed by a variety of 3D analysis techniques to determine the optimum parameters for identification of abnormal segments when compared with coronary arteriography and left ventriculography. Left ventricular wall motion was quantified by calculation of regional ejection fraction (rEF) through use of count, volume, and cord length changes from end diastole to end systole. Three contractile models were evaluated: (1) fixed center of mass (COM), (2) floating COM, and (3) a modification of the method developed by Slager et al (J Am Coll Cardiol 1986;7:317-26), based on the motion of implanted endocardial markers. Eight, twelve, and eighteen 3D segments were analyzed by means of the 3 contractile models and correlated against coronary artery disease assessed by coronary arteriography. Single-head gamma-camera acquisition provided adequate counting statistics to reliably compute rEF for up to 18 left ventricular segments. Using count changes the overall results were able to identify myocardium supplied by diseased coronary arteries when compared with coronary arteriography. Cord length and, to a lesser degree, volume changes provided somewhat poorer sensitivities and specificities when compared with rEF computed from regional count changes, as compared with coronary arteriography. CONCLUSIONS: Three-dimensional quantitative GBPS appears to be a sensitive method for assessing wall motion defects due to coronary artery disease.
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