Abdominal Sound Speed Estimation through Machine Learning
Abstract
In clinical ultrasound, the assumption of a uniform sound speed (SoS) of 1540 m/s can lead to errors, especially in areas with bone or bowel gas. Through-transmission ultrasound can produce quantitative SoS images, but waveform inversion (WI) methods used for refining SoS models rely heavily on an accurate initial model. This work presents a customized U-Net neural network trained on synthetic ultrasound data to provide more accurate initial SoS models for abdominal cross-sections, improving WI convergence. Forward-simulated acoustic data were generated using a 64-element semicircular array, and the neural network estimated SoS maps from radial B-mode lines. The network's performance was evaluated using Dice coefficient, SSIM, PSNR, and SoS difference metrics, showing superior results compared to a uniform 1540 m/s model. Hyperparameter tuning demonstrated that deeper networks with dropout rates of 50% achieved optimal performance. Overall, our findings suggest that neural networks can enhance WI processes by offering more accurate SoS models than traditional homogeneous assumptions.
Publication Title
IEEE Ultrasonics Ferroelectrics and Frequency Control Joint Symposium Uffc Js 2024 Proceedings
Recommended Citation
Park, J., Wickman, R., Yunis, O., Hyun, D., Preza, C., & Herickhoff, C. (2024). Abdominal Sound Speed Estimation through Machine Learning. IEEE Ultrasonics Ferroelectrics and Frequency Control Joint Symposium Uffc Js 2024 Proceedings https://doi.org/10.1109/UFFC-JS60046.2024.10794120
