Technical Note: Performance evaluation of volumetric imaging based on motion modeling by principal component analysis
Technical Note: Performance evaluation of volumetric imaging based on motion modeling by principal component analysis
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DOI：
10.1002/mp.16123
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发表时间：
2022-12-03
期刊：
影响因子：
3.8
通讯作者：
Miyamoto，Naoki
中科院分区：
文献类型：
--
作者：
Asano，Suzuka;Oseki，Keishi;Miyamoto，Naoki
目的定量评估基于主成分分析（PCA）肺运动建模的容积成像的可实现性能。方法在基于PCA的容积成像中，内部变形被表示为从患者特定的四维计算机断层扫描（4DCT）数据集评估的变形向量场的PCA导出的特征向量的线性组合。通过使用最佳主成分系数（PC）评估的变形向量场来变形参考CT图像，从而合成体积图像。假设较大的PC再现比原始4DCT数据集中包含的变形更大的变形。为了评价PCA重建的体积图像的再现性，合成尽可能接近地面真理，平均绝对误差（MAE），结构相似性指数测量（SSIM）和隔膜位置的差异进行了评价，使用22个4DCT数据集的9 patients.ResultsMean MAE和SSIM值的PCA重建的体积图像分别约为80 HU和0.88，无论呼吸阶段如何。在包括运动范围超过建模数据的数据的大多数测试情况下，隔膜的位置误差小于5 mm。结果表明，可以再现建模4DCT数据集中不包括的大变形。此外，由于第一PC与横膈膜位置的位移相关，因此第一特征向量成为表示呼吸相关变形的主导因素。然而，其他PC不一定以与第一PC相同的趋势变化，并且在系数之间没有观察到相关性。因此，随机分配或采样这些PC在扩展范围内可能适用于合理地生成增强数据集与各种deformation.ConclusionsReasonable图像合成的准确性相比，在以前的研究中，通过使用临床数据。这些结果表明基于PCA的体积成像在临床应用中的潜力。
PurposeTo quantitatively evaluate the achievable performance of volumetric imaging based on lung motion modeling by principal component analysis (PCA).MethodsIn volumetric imaging based on PCA, internal deformation was represented as a linear combination of the eigenvectors derived by PCA of the deformation vector fields evaluated from patient‐specific four‐dimensional‐computed tomography (4DCT) datasets. The volumetric image was synthesized by warping the reference CT image with a deformation vector field which was evaluated using optimal principal component coefficients (PCs). Larger PCs were hypothesized to reproduce deformations larger than those included in the original 4DCT dataset. To evaluate the reproducibility of PCA‐reconstructed volumetric images synthesized to be close to the ground truth as possible, mean absolute error (MAE), structure similarity index measure (SSIM) and discrepancy of diaphragm position were evaluated using 22 4DCT datasets of nine patients.ResultsMean MAE and SSIM values for the PCA‐reconstructed volumetric images were approximately 80 HU and 0.88, respectively, regardless of the respiratory phase. In most test cases including the data of which motion range was exceeding that of the modeling data, the positional error of diaphragm was less than 5 mm. The results suggested that large deformations not included in the modeling 4DCT dataset could be reproduced. Furthermore, since the first PC correlated with the displacement of the diaphragm position, the first eigenvector became the dominant factor representing the respiration‐associated deformations. However, other PCs did not necessarily change with the same trend as the first PC, and no correlation was observed between the coefficients. Hence, randomly allocating or sampling these PCs in expanded ranges may be applicable to reasonably generate an augmented dataset with various deformations.ConclusionsReasonable accuracy of image synthesis comparable to those in the previous research were shown by using clinical data. These results indicate the potential of PCA‐based volumetric imaging for clinical applications.