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.
翻译:衰减系数(AC)是测量组织声学特性的基本尺度,可用于医学诊断。在这项工作中,我们调查使用进化神经网络(CNNs)从无线电频率超声波信号直接估计AC的可行性。为了开发CNNs,我们用从组织模拟数字幻影中收集的RF信号来测定AC值,范围在0.1至1.5 dB/(MHz*cm)之间。模型是根据1-D的RF数据拼合点来培训的。我们获得的精确AC估计误差为0.08、0.12、0.12、0.20、0.25,其长度分别为10毫米、5毫米、2毫米和1毫米。我们通过直观与进化过滤器相关的频率内容来解释模型的性能。我们的研究显示,AC可以使用深层次的学习来计算,CNN的重量可以进行物理解释。