Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging.
Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging.
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这项工作的目的是回顾轴向质心(COM)测量的数据驱动方法，以从列表模式采集的精细采样(100ms)单光子发射计算机断层扫描(SPECT)投影数据中恢复替代呼吸信号。在我们的初步评估中，我们从一个安装在类星体呼吸运动平台上的拟人心脏模型获取了列表模式投影数据，模拟了15 mm幅度的呼吸运动。我们还选择了302名连续的患者(138名男性，164名女性)进行列表模式采集、外部呼吸运动跟踪和书面同意，以评估我们的数据驱动方法的临床效果。对于单独的投影数据集和整个患者组，计算了通过视觉跟踪系统(VTS)获得的呼吸信号与COM测量之间的线性回归、皮尔逊相关系数(R)和估计的标准误差(参见)。VTS和COM得到的呼吸信号都被用来估计和校正呼吸运动。六自由度刚体运动估计的重建方法有两种：(1)使用3次有序子集期望最大化(OSEM)迭代和12次最大似然期望最大化(MLEM)迭代。呼吸运动补偿分别使用16个子集(每个子集4个投影角)和5次迭代的OSEM，或者分别使用两个呼吸估计的MLEM和80次迭代。还进行了极图量化，计算不包括呼吸运动和有呼吸运动的极图之间的百分比计数差(%diff)。计算17个节段的平均差值百分比(根据ASNC指南定义)。采用配对t检验确定显著性(p值)。当比较VTS和COM呼吸信号时，计算的r值在−0.0 1和0.96之间变化很大，平均为0.70，而我们的患者组的SEE在0.80和6.48 mm之间变化，平均为2.0 5 mm，而一个拟人模型采集的相同值分别为0.91和1.11 mm。比较VTS和COM在S-I方向上的呼吸运动估计，OEM(MLEM)的r=0.9(0.94)，SEE为1.56 mm(1.20 mm)。Bland-Altman曲线图和计算的组内相关系数也表明VTS和COM呼吸运动估计值之间具有很好的一致性。OEM和MLEM的VTS(COM)的平均S-I呼吸估计值分别为9.04(9.2 mm)和9.01 mm(9.14 mm)。当比较VTS和COM估计的呼吸信号时，配对t检验接近有意义，OSEM和MLEM的p值分别为0.069和0.051。使用VTS(COM)的拟人心脏体模实验估计的呼吸分别为12.62(14.10 mm)和12.55 mm(14.29 mm)。当使用VTS得出的呼吸估计值来校正呼吸时，与COM得出的估计值相比，极地图量化产生的平均%差异一致地更好。结果表明，我们的COM方法有可能提供一个自动的数据驱动的心脏呼吸运动校正，而不是我们的VTS方法的缺陷。然而，在修正程度上，它与VTS方法一般不是等同的。
The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100 ms) single photon emission computed tomography (SPECT) projection data derived from list-mode acquisitions. For our initial evaluation, we acquired list-mode projection data from an anthropomorphic cardiac phantom mounted on a Quasar respiratory motion platform simulating 15 mm amplitude respiratory motion. We also selected 302 consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson’s correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements were calculated for individual projection data sets and for the patient group as a whole. Both the VTS- and COM-derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom rigid-body motion estimation was done in two ways: (1) using three iterations of ordered-subsets expectation-maximization (OSEM) with four subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (four projection angles per subset) and five iterations or MLEM and 80 iterations, using the two respiratory estimates, respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff was calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values). The r-value calculated when comparing the VTS and COM respiratory signals varied widely between −0.01 and 0.96 with an average of 0.70, while the SEE varied between 0.80 and 6.48 mm with an average of 2.05 mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11 mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r = 0.90 (0.94), and an SEE of 1.56 mm (1.20 mm) for OSEM (MLEM), respectively. Bland–Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 (9.2 mm) and 9.01 mm (9.14 mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 (14.10 mm) and 12.55 mm (14.29 mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS-derived respiratory estimates to correct for respiration compared to the COM-derived estimates. The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the drawbacks of our VTS methodology. However, it is not generally equivalent to the VTS method in extent of correction.
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