IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Dec;69(12):3232-3242. doi: 10.1109/TUFFC.2022.3210701. Epub 2022 Nov 24.
Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and body. Flexible probes, which can deform and effectively adhere to the body, are a promising technology for long-term monitoring applications. However, owing to the flexible element array geometry, the reconstructed image becomes blurred and distorted. In this study, we propose a flexible probe U.S. imaging method based on element array geometry estimation from radio frequency (RF) data using a deep neural network (DNN). The input and output of the DNN are the RF data and parameters that determine the element array geometry, respectively. The DNN was first trained from scratch with simulation data and then fine-tuned with in vivo data. The DNN performance was evaluated according to the element position mean absolute error (MAE) and the reconstructed image quality. The reconstructed image quality was evaluated with peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM). In the test conducted with simulation data, the average element position MAE was 0.86 mm, and the average reconstructed image PSNR and MSSIM were 20.6 and 0.791, respectively. In the test conducted with in vivo data, the average element position MAE was 1.11 mm, and the average reconstructed image PSNR and MSSIM were 19.4 and 0.798, respectively. The average estimation time was 0.045 s. These results demonstrate the feasibility of the proposed method for long-term real-time monitoring using flexible probes.
传统上，超声（US）诊断使用手持刚性探头进行。由于这些设备需要持续按压在身体上以去除探头和身体之间的空气，因此很难用于长期监测。能够变形并有效贴合身体的柔性探头是用于长期监测应用的有前途的技术。然而，由于柔性元件阵列几何形状，重建图像变得模糊和失真。在这项研究中，我们提出了一种基于从射频 (RF) 数据使用深度神经网络 (DNN) 估计元件阵列几何形状的柔性探头 US 成像方法。DNN 的输入和输出分别是 RF 数据和确定元件阵列几何形状的参数。DNN 首先使用模拟数据从头开始进行训练，然后使用体内数据进行微调。根据元件位置平均绝对误差 (MAE) 和重建图像质量来评估 DNN 的性能。使用峰值信噪比 (PSNR) 和平均结构相似性 (MSSIM) 评估重建图像质量。在使用模拟数据进行的测试中，平均元件位置 MAE 为 0.86mm，平均重建图像 PSNR 和 MSSIM 分别为 20.6 和 0.791。在使用体内数据进行的测试中，平均元件位置 MAE 为 1.11mm，平均重建图像 PSNR 和 MSSIM 分别为 19.4 和 0.798。平均估计时间为 0.045s。这些结果证明了使用柔性探头进行长期实时监测的方法的可行性。
