Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3895-3898. doi: 10.1109/EMBC48229.2022.9871532.
Reverberant Shear Wave Elastography (RSWE) is an ultrasound elastography technique that offers great advantages, however, current estimators generate underestimations and time-consuming issues. As well, the involvement of Deep Learning into the medical imaging field with new tools to assess complex problems, makes it a great candidate to serve as a new approach for a RSWE estimator. This work addresses the application of a Deep Neural Network (DNN) for the estimation of Shear Wave Speed (SWS) maps from particle velocity using numerically simulated data. The architecture of the proposed network is based on a U-Net, which works with a custom loss function specifically adopted for the reconstruction task. Four DNNs were trained using four different databases: clean, noisy, acquired at variable frequency, and noisy and acquired at variable frequency data. After the training of the DNNs, the predicted SWS maps were evaluated based on different metrics related to segmentation, regression and similarity of images. The model for clean data showed better results with a Mean Absolute Error (MAE) of 0.011, Mean Square Error(MSE) of 0.001, modified Intersection over Union (mIoU) of 98.4%, Peak Signal to Noise Ratio (PSNR) of 32.925 and a Structural Similarity Index Measure (SSIM) of 0.99, for 250 (size of Testing Sets); while the other models delivered SSIM in the range of 0.87 to 0.96. It was concluded that noisy and clean data could be effectively handled by the model, while the other ones still need enhancement. Clinical Relevance- This work is focused on the application of a Deep Learning approach to accurately asses the Shear Wave Speed in numerical simulations of Reverberant Shear Wave Elastography approach. This novel estimator could be useful for future clinical experiments specially with real time applications to determine the status of living tissue such as detection of malignant or benign tumors located in breast cervix prostate or skin and in the diagnosis of other pathologies such us liver fibrosis.
背向散射剪切波弹性成像（RSWE）是一种超声弹性成像技术，具有很大的优势，然而，当前的估计方法存在低估和耗时的问题。此外，深度学习技术应用于医学成像领域，为评估复杂问题提供了新的工具，使其成为 RSWE 估计器的新方法的理想选择。本工作针对的是应用深度神经网络（DNN）从粒子速度估计剪切波速度（SWS）图，使用数值模拟数据。所提出的网络的架构基于 U-Net，它与一个专门为重建任务而采用的自定义损失函数一起工作。四个 DNN 使用四个不同的数据库进行训练：干净数据、噪声数据、在不同频率下采集的数据和在噪声和不同频率下采集的数据。在 DNN 训练后，根据与分割、回归和图像相似性相关的不同指标来评估预测的 SWS 图。对于干净数据的模型，其结果更好，具有 0.011 的平均绝对误差（MAE）、0.001 的均方误差（MSE）、98.4%的修正交并比（mIoU）、32.925 的峰值信噪比（PSNR）和 0.99 的结构相似性指数度量（SSIM），对于 250 个测试集；而其他模型的 SSIM 则在 0.87 到 0.96 之间。研究结论认为，模型可以有效地处理噪声和干净数据，而其他数据仍需要进一步增强。临床意义-本工作重点研究了深度学习方法在背向散射剪切波弹性成像方法数值模拟中对剪切波速度进行准确评估的应用。这种新的估计器可能对未来的临床实验有用，特别是对于实时应用，以确定活体组织的状态，如检测位于乳房、宫颈、前列腺或皮肤的恶性或良性肿瘤，以及在诊断其他病变如肝纤维化等方面。
