Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps
Fully Automated Data-Driven Respiratory Signal Extraction From SPECT Images Using Laplacian Eigenmaps
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DOI：
10.1109/tmi.2016.2576899
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发表时间：
2016-11-01
影响因子：
10.6
通讯作者：
Maier, Andreas K.
中科院分区：
文献类型：
--
作者：
Sanders, James C.;Ritt, Philipp;Maier, Andreas K.
我们提出了一种数据驱动的方法，用于从SPECT列表模式数据中提取呼吸替代信号。该方法是基于拉普拉斯特征映射的降维。通过自适应设置尺度参数并添加一系列后处理步骤来校正投影之间的极性和归一化，我们实现了全自动操作并为整个SPECT采集提供呼吸替代信号。我们使用来自三种采集类型（心肌灌注、肝分流诊断、肺吸入/灌注）的67例患者扫描和作为金标准的Anzai压力带验证了该方法。所提出的方法实现了与Anzai的平均相关性为0.81 +/- 0.17（中值0.89）。在随后的分析中，我们描述了该方法在计数率方面的性能，并描述了一个用于识别统计数据不足的扫描的预测器。据我们所知，这是迄今为止发表的用于SPECT的数据驱动呼吸信号提取方法的第一次大规模验证，并且我们的结果与文献中报道的应用于其他模态（如MR和PET）的此类技术的结果进行了比较。
We propose a data-driven method for extracting a respiratory surrogate signal from SPECT list-mode data. The approach is based on dimensionality reduction with Laplacian Eigenmaps. By setting a scale parameter adaptively and adding a series of post-processing steps to correct polarity and normalization between projections, we enable fully-automatic operation and deliver a respiratory surrogate signal for the entire SPECT acquisition. We validated the method using 67 patient scans from three acquisition types (myocardial perfusion, liver shunt diagnostic, lung inhalation/perfusion) and an Anzai pressure belt as a gold standard. The proposed method achieved a mean correlation against the Anzai of 0.81 +/- 0.17 (median 0.89). In a subsequent analysis, we characterize the performance of the method with respect to count rates and describe a predictor for identifying scans with insufficient statistics. To the best of our knowledge, this is the first large validation of a data-driven respiratory signal extraction method published thus far for SPECT, and our results compare well with those reported in the literature for such techniques applied to other modalities such as MR and PET.