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Separation of factor images for blood flow estimation in positron emission tomography using ensemble independent component analysis

机译:基于集合独立分量分析的正电子发射断层扫描中用于血流估计的因子图像分离

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To calculate regional myocardial blood flow, ensemble ICA (independent component analysis) was evaluated. Myocardial blood flow was estimated from dynamic H/sub 2//sup 15/O PET, and perfusion score was computed from myocardial SPECT data in this study. In ensemble ICA, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Major cardiac components were separated successfully by the ensemble ICA method. Mean myocardial blood flow was 1.2/spl plusmn/0.40 ml/min/g in rest, 1.85/spl plusmn/1.12 ml/min/g in stress state. Reproducibility of data analysis for blood flow values were highly correlated (r=0.99). The image contrast between left ventricle and myocardium was better than other method. Perfusion reserve was decreased in the stenosis region. Applied ensemble ICA method will be a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.
机译:为了计算局部心肌血流量,对整体ICA(独立成分分析)进行了评估。通过动态H / sub 2 // sup 15 / O PET估算心肌血流量,并根据该研究中的心肌SPECT数据计算灌注评分。在集成ICA中,后pdf通过校正的高斯分布来近似,以纳入非负约束,这适用于核医学中的动态图像。通过集成ICA方法成功分离出主要的心脏成分。静息状态下的平均心肌血流量为1.2 / spl plusmn / 0.40 ml / min / g,在应激状态下为1.85 / spl plusmn / 1.12 ml / min / g。血流数据分析的可重复性高度相关(r = 0.99)。左心室与心肌之间的图像对比度优于其他方法。狭窄区域的灌注储备减少。应用集成ICA方法将是处理通过核医学技术获得的动态图像序列的可行方法。

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