To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients (fv) to account for most of the spectrum energy (Σfv2). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves (R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors, providing an automatic robust tool to evaluate diaphragm motion.

中文翻译：

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通过机器学习从 4DCT 图像自动评估平均隔膜运动轨迹

基于四维计算机断层扫描（4DCT）自动估计平均膈肌运动轨迹（ADMT），促进呼吸运动和运动变化的临床评估和回顾性运动研究。我们开发了一种有效的运动提取方法和一种基于机器学习的算法来估计 ADMT。研究了 11 名患者的 22 组 4DCT 图像（模拟时为 4DCT1，治疗时为 4DCT2）。在自动分割肺后，左肺和右肺的每片微分容积 (dVPS) 曲线被计算为与全呼气相关的每个阶段的片数的函数。进行5切片移动平均后，应用离散余弦变换（DCT）分析频域中的dVPS曲线。通过使用几个最低频率系数 (fv) 来解释大部分频谱能量 (Σfv2)，从而降低了频谱数据的维数。然后应用多元线性回归 (MLR) 方法通过拟合地面实况 - 测量的 ADMT 来确定这些频率的权重，ADMT 由每侧隔膜的三个枢轴点表示。“留一法”交叉验证方法用于分析三个图像集预测结果的统计性能：4DCT1、4DCT2 和 4DCT1 + 4DCT2。发现 DCT 域中的七个最低频率足以近似患者的 dVPS 曲线（MLR 拟合中的 R = 91%-96%）。使用留一法预测的 ADMT 中，左侧隔膜的平均误差为 0.3 ± 1.9 mm，右侧隔膜的平均误差为 0.0 ± 1.4 mm。4DCT2的预测误差低于4DCT1，4DCT1和4DCT2组合的预测误差最低。这种基于频率分析的机器学习技术被用来自动预测 ADMT，误差在可接受的范围内 (0.2 ± 1.6 mm)。这种体积方法不受肺部肿瘤存在的影响，提供了一种自动强大的工具来评估横膈膜运动。