Estimating the ultrasound attenuation coefficient using convolutional neural networks -- a feasibility study

Autor:	Jarosik, Piotr, Byra, Michal, Lewandowski, Marcin, Klimonda, Ziemowit
Rok vydání:	2022
Předmět:	Physics - Medical Physics Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.09533 Zobrazit plný text záznamu View this record from Arxiv