论文标题
使用卷积神经网络估算超声衰减系数 - 可行性研究
Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study
论文作者
论文摘要
衰减系数（AC）是组织声学特性的基本量度，可用于医学诊断。在这项工作中，我们研究了使用卷积神经网络（CNN）直接从射频（RF）超声信号估算AC的可行性。为了开发CNN，我们使用了从模仿数值幻象的组织中收集的RF信号，以在0.1至1.5 dB/（MHz*cm）的范围内为AC值收集。根据RF数据的1D贴片对模型进行了培训。我们获得的平均绝对AC估计误差分别为0.08、0.12、0.20、0.25，分别为10 mm，5 mm，2 mm和1 mm。我们通过可视化与卷积过滤器相关的频率内容来解释模型的性能。我们的研究表明，可以使用深度学习来计算AC，并且CNN的权重可以具有物理解释。
Attenuation coefficient (AC) is a fundamental measure of tissue acoustical properties, which can be used in medical diagnostics. In this work, we investigate the feasibility of using convolutional neural networks (CNNs) to directly estimate AC from radio-frequency (RF) ultrasound signals. To develop the CNNs we used RF signals collected from tissue mimicking numerical phantoms for the AC values in a range from 0.1 to 1.5 dB/(MHz*cm). The models were trained based on 1-D patches of RF data. We obtained mean absolute AC estimation errors of 0.08, 0.12, 0.20, 0.25 for the patch lengths: 10 mm, 5 mm, 2 mm and 1 mm, respectively. We explain the performance of the model by visualizing the frequency content associated with convolutional filters. Our study presents that the AC can be calculated using deep learning, and the weights of the CNNs can have physical interpretation.
