A deep learning framework to estimate elastic modulus from ultrasound measured displacement fields
A deep learning framework to estimate elastic modulus from ultrasound measured displacement fields
复制标题
从超声测量的位移场估计弹性模量的深度学习框架
DOI：
10.1117/12.2654675
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Richards, Michael S.
中科院分区：
文献类型：
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作者：
Tuladhar, Utsav Ratna;Simon, Richard A.;Linte, Cristian A.;Richards, Michael S.
超声（US）弹性成像是一种能够从变形组织的超声图像中无创量化材料特性（如刚度）的技术。使用图像匹配算法从US图像测量位移场，然后推断或随后测量参数（通常是弹性模量）以识别潜在的组织病理，例如癌组织。几个传统的反问题的方法，松散地分组为直接或迭代，已被探索估计的弹性模量。然而，迭代技术通常是缓慢的且计算密集的，而直接技术虽然在计算上更高效，但对测量噪声非常敏感并且需要完整的位移场数据（即，两个矢量分量）。在这项工作中，我们提出了一种深度学习方法来解决逆问题，并从美国测量的位移场的一个分量中恢复弹性模量的空间分布。这里使用的神经网络仅使用通过正向有限元（FE）模型获得的模拟数据进行训练，该模型具有模量场的已知变化，从而避免了对可能具有挑战性的大型测量数据集的依赖。然后使用基于U网的神经网络来预测模量分布（即，求解逆问题）。我们用模拟测试数据集定量评估了我们的训练模型，并观察到重建的弹性模量和真实弹性模量之间的均方误差（MSE）为0.0018，平均绝对百分比误差（MAPE）为1.14%。此外，我们还定性地比较了我们的U-网络模型的输出，实验测量的位移数据采集使用美国弹性成像组织模仿校准体模。
Ultrasound (US) elastography is a technique that enables non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The displacement field is measured from the US images using image matching algorithms, and then a parameter, often the elastic modulus, is inferred or subsequently measured to identify potential tissue pathologies, such as cancerous tissues. Several traditional inverse problem approaches, loosely grouped as either direct or iterative, have been explored to estimate the elastic modulus. Nevertheless, the iterative techniques are typically slow and computationally intensive, while the direct techniques, although more computationally efficient, are very sensitive to measurement noise and require the full displacement field data (i.e., both vector components). In this work, we propose a deep learning approach to solve the inverse problem and recover the spatial distribution of the elastic modulus from one component of the US measured displacement field. The neural network used here is trained using only simulated data obtained via a forward finite element (FE) model with known variations in the modulus field, thus avoiding the reliance on large measurement data sets that may be challenging to acquire. A U-net based neural network is then used to predict the modulus distribution (i.e., solve the inverse problem) using the simulated forward data as input. We quantitatively evaluated our trained model with a simulated test dataset and observed a 0.0018 mean squared error (MSE) and a 1.14% mean absolute percent error (MAPE) between the reconstructed and ground truth elastic modulus. Moreover, we also qualitatively compared the output of our U-net model to experimentally measured displacement data acquired using a US elastography tissue-mimicking calibration phantom.
DOI：
10.1109/ieeeconf51394.2020.9443450
发表时间：
2020-10
期刊：
2020 54th Asilomar Conference on Signals, Systems, and Computers
影响因子：
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作者：
N. Mohammadi;M. Doyley;M. Çetin
通讯作者：
N. Mohammadi;M. Doyley;M. Çetin
超声弹性成像中的深度学习：综述。
DOI：
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发表时间：
2022
期刊：
Medical Physics (Lancaster)
影响因子：
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作者：
Hongliang Li;M. Bhatt;Zhen Qu;Shiming Zhang;Martin C. Hartel;A. Khademhosseini;G. Cloutier
通讯作者：
G. Cloutier