Santhanam Anand P, Stiehl Brad, Lauria Michael, Hasse Katelyn, Barjaktarevic Igor, Goldin Jonathan, Low Daniel A
Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Department of Pulmonary Critical Care, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Med Phys. 2021 Feb;48(2):667-675. doi: 10.1002/mp.14252. Epub 2020 Dec 22.
Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain.
In this paper, we present a machine learning-based method that predicts the three-dimensional (3D) lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed five-dimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground-truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath-hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-norm-based direct comparison was employed between the estimated elasticity and the ground-truth elasticity. In the second approach, we generated a synthetic four-dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the ground-truth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC).
The results show that a cGAN-based machine learning approach was effective in computing the lung tissue elasticity given the end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44 ± 0.2 KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87 ± 0.4 KPa. These results show that the cGAN-generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN-generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity-generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity-generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity-generated end inhalation CT and the ground-truth end inhalation CT.
The cGAN-generated lung tissue elasticity given an end-expiration CT image can be computed in near real time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end-expiration CT image within clinically acceptable numerical accuracy.
肺部弹性成像旨在测量肺实质组织弹性，其应用范围涵盖从诊断目的到生物力学引导的变形。表征肺组织弹性需要四维（4D）肺部运动作为输入，目前通过对4D计算机断层扫描（4DCT）数据集进行可变形配准来估计。由于4DCT成像仅在放射治疗设置中广泛使用，因此需要在缺乏4D成像的情况下预测弹性分布，以用于放射治疗领域内外的应用。
在本文中，我们提出了一种基于机器学习的方法，该方法可预测给定呼气末3DCT的三维（3D）肺组织弹性分布。从呼气末3DCT预测肺组织弹性的方法采用了深度神经网络，该网络可预测给定CT数据集的组织弹性。为了进行训练和验证，我们使用了五维CT（5DCT）数据集和有限元生物力学肺模型。首先使用5DCT模型生成呼气末肺几何形状，将其作为生物力学建模的源肺几何形状。从5DCT模型计算出从呼气末到吸气末的变形矢量场，并将其作为输入，以求解肺组织弹性。采用了逆弹性估计过程，我们迭代求解肺弹性分布，直到模型再现真实的变形矢量场。机器学习过程使用一种特定类型的学习过程，即约束广义对抗神经网络（cGAN），它以监督方式学习肺组织弹性。生物力学估计的组织弹性与呼气末CT一起作为监督学习的输入。经过训练的cGAN从给定的屏气CT图像生成弹性。通过两种方法对估计的弹性进行了验证。在第一种方法中，在估计的弹性与真实弹性之间进行基于L2范数的直接比较。在第二种方法中，我们使用肺生物力学模型和估计的弹性生成合成四维CT（4DCT），并使用三种图像相似性度量：互信息（MI）、结构相似性指数（SSIM）和归一化互相关（NCC），将变形与真实的4D变形进行比较。
结果表明，基于cGAN的机器学习方法在给定呼气末CT数据集的情况下，能够有效地计算肺组织弹性。对于训练数据集，我们获得的学习精度为0.44±0.2kPa。对于由13个4D数据集组成的验证数据集，我们能够获得0.87±0.4kPa的精度。这些结果表明，cGAN生成的弹性与潜在的真实弹性相关性良好。然后我们将估计的弹性与生物力学模型集成，并应用相同的边界条件以生成吸气末CT。cGAN生成的图像与原始吸气末CT非常相似。MI的平均值为1.77，表明真实情况与cGAN弹性生成的吸气末CT数据之间具有高度的局部对称性。观察到13名患者的结构相似性平均值为0.89，表明cGAN弹性生成的吸气末CT具有高度的结构完整性。最后，平均NCC值为0.97，表明cGAN弹性生成的吸气末CT与真实吸气末CT在对比度和亮度上可能存在差异。
给定呼气末CT图像，cGAN生成的肺组织弹性可以近实时计算。使用肺组织弹性以及生物力学模型，可以在临床可接受的数值精度范围内，从给定的呼气末CT图像生成4D肺变形。
