Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study
This is an incremental feasibility study for medical diagnostics, potentially improving tissue characterization in ultrasound imaging.
The study tackled the problem of estimating the ultrasound attenuation coefficient (AC) from radio-frequency signals using convolutional neural networks (CNNs), achieving mean absolute errors ranging from 0.08 to 0.25 dB/(MHz*cm) for different patch lengths.
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.