TY - GEN
T1 - Deep Learning based Ultrasound Computed Tomography for Real-Time Construction
AU - Gao, Qinhan
AU - Almekkawy, Mohamed
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Conventional Ultrasound Computed Tomography (UCT) techniques, which rely on iterative optimization methods, are hindered by their high computational demands, making real-time processing impractical. To address this issue, we introduce an innovative deep learning-based solution that directly addresses the mapping between the received ultrasound signals and Speed of Sound (SoS) distribution images. Our approach employs a Convolutional Neural Network (CNN) that incorporates both 1D and 2D convolutions to process the input signals. Simulation data were generated using the k-wave open-source acoustic wave solver to simulate the wave equation. Remarkably, our experimental outcomes show the effectiveness of the proposed neural network in generating highly accurate SoS images, all achieved within a significantly shorter timeframe than traditional methods.
AB - Conventional Ultrasound Computed Tomography (UCT) techniques, which rely on iterative optimization methods, are hindered by their high computational demands, making real-time processing impractical. To address this issue, we introduce an innovative deep learning-based solution that directly addresses the mapping between the received ultrasound signals and Speed of Sound (SoS) distribution images. Our approach employs a Convolutional Neural Network (CNN) that incorporates both 1D and 2D convolutions to process the input signals. Simulation data were generated using the k-wave open-source acoustic wave solver to simulate the wave equation. Remarkably, our experimental outcomes show the effectiveness of the proposed neural network in generating highly accurate SoS images, all achieved within a significantly shorter timeframe than traditional methods.
UR - http://www.scopus.com/inward/record.url?scp=85178646381&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178646381&partnerID=8YFLogxK
U2 - 10.1109/IUS51837.2023.10307013
DO - 10.1109/IUS51837.2023.10307013
M3 - Conference contribution
AN - SCOPUS:85178646381
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2023 - IEEE International Ultrasonics Symposium, Proceedings
PB - IEEE Computer Society
T2 - 2023 IEEE International Ultrasonics Symposium, IUS 2023
Y2 - 3 September 2023 through 8 September 2023
ER -