IEEE Trans Med Imaging. 2016 Nov;35(11):2425-2435. doi: 10.1109/TMI.2016.2576899. Epub 2016 Jun 7.
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.
我们提出了一种从 SPECT 列表模式数据中提取呼吸替代信号的基于数据驱动的方法。该方法基于拉普拉斯特征映射的降维。通过自适应设置比例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 采集提供了呼吸替代信号。我们使用来自三种采集类型（心肌灌注、肝脏分流诊断、肺吸入/灌注）的 67 个患者扫描和 Anzai 压力带作为金标准来验证该方法。该方法与 Anzai 的平均相关性为 0.81 ± 0.17（中位数为 0.89）。在随后的分析中，我们根据计数率来描述该方法的性能，并描述了一种用于识别统计数据不足的扫描的预测器。据我们所知，这是迄今为止对 SPECT 中基于数据的呼吸信号提取方法进行的首次大型验证，我们的结果与文献中报道的应用于其他模态（如 MR 和 PET）的此类技术的结果相当。
