首页> 外文期刊>BioMed research international >Fully Automated Quantification of the Striatal Uptake Ratio of [~(99m)Tc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson's Disease and the Temporal Regression of Striatal Tracer Uptake
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Fully Automated Quantification of the Striatal Uptake Ratio of [~(99m)Tc]-TRODAT with SPECT Imaging: Evaluation of the Diagnostic Performance in Parkinson's Disease and the Temporal Regression of Striatal Tracer Uptake

机译:与SPECT成像的[〜(99M)TC] -Trodat的纹纹纹状比的全自动量化:评估帕金森病的诊断性能和纹纹纹状体吸收的时间回归

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Purpose. We aimed at improving the existing methods for the fully automatic quantification of striatal uptake of [~(99m)Tc]-TRODAT with SPECT imaging. Procedures. A normal [~(99m)Tc] -TRODAT template was first formed based on 28 healthy controls. Images from PD patients (n = 365) and nPD subjects (28 healthy controls and 33 essential tremor patients) were spatially normalized to the normal template. We performed an inverse transform on the predefined striatal and reference volumes of interest (VOIs) and applied the transformed VOIs to the original image data to calculate the striatal-to-reference ratio (SRR). The diagnostic performance of the SRR was determined through receiver operating characteristic (ROC) analysis. Results. The SRR measured with our new and automatic method demonstrated excellent diagnostic performance with 92% sensitivity, 90% specificity, 92% accuracy, and an area under the curve (AUC) of 0.94. For the evaluation of the mean SRR and the clinical duration, a quadratic function fit the data with R~2 = 0.84. Conclusions. We developed and validated a fully automatic method for the quantification of the SRR in a large study sample. This method has an excellent diagnostic performance and exhibits a strong correlation between the mean SRR and the clinical duration in PD patients.
机译:目的。我们旨在改善具有SPECT成像的[〜(99M)TC] -TRODAT的全自动量化的现有方法。程序。首先基于28个健康对照组形成正常的[〜(99m)Tc] -trodat模板。来自PD患者的图像(n = 365)和NPD受试者(28个健康对照和33项基本震颤患者)在空间上标准化为正常模板。我们对预定义的纹纹版和参考体积(VoIS)进行了反转变换,并将转换的VoIS应用于原始图像数据以计算划分基准比(SRR)。通过接收器操作特征(ROC)分析确定SRR的诊断性能。结果。通过我们的新和自动方法测量的SRR显示出优异的诊断性能,灵敏度为92%,特异性90%,精度为92%,精度为0.94的曲线(AUC)。为了评估平均SRR和临床持续时间,二次函数适合R〜2 = 0.84的数据。结论。我们开发并验证了一个全自动的方法,用于在大型研究样本中定量SRR。该方法具有出色的诊断性能,并且在PD患者中表现出平均SRR与临床持续时间之间的强烈相关性。

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