Ms. Ning Shen Profile

Ning Shen

at Donghua Univ

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Proceedings Article | 4 April 2022 Presentation + Paper

3D ultrasound computed tomography system calibration using a neural network

Hongjian Wang, Ning Shen, Xiaoxu Lei, Hartmut Gemmeke, Michael Zapf, Shoujian Yu, Xiaoling Xia

Proceedings Volume 12038, 120380B (2022) https://doi.org/10.1117/12.2607563

KEYWORDS: Calibration, Transducers, Neural networks, Ultrasonography, Computing systems, Computed tomography, 3D image processing, Receivers, Iterative methods, 3D metrology

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In a three-dimensional ultrasound computed tomography (3D USCT) system, system errors such as transducer delay, transducer position deviation and temperature error will affect the quality of reconstructed images. Most of the existing calibration works use iterative methods to solve large-scale systems of linear equations. In our case, the transducer delay and position deviation calibration problem of the considered 3D USCT system is essentially to solve a linear system containing about 840,000 equations and 11,500 unknowns. For such a large system, the existing iterative methods require a lot of computation time and the accuracy also needs to be improved. Considering that neural networks have the ability to find optimized solutions for large-scale linear systems, we propose a neural network method for transducer delay and position deviation calibration. We designed a neural network to calibrate both delay and position solutions, together during the network training. We test the method with simulated system data where we add transducer delays in the range of 0.7~1.3 μs, position deviation in the range of -1~1 mm for the X- and Y-axis, and -0.3~0.3 mm for the Z-axis. Results show that the mean delay error is reduced to 0.15 μs, and the mean position error is reduced to 0.15 mm, after a neural network calibration process which takes about 11 minutes. The delay calibration result is better than the existing Newton method in literature, while our method is especially less time-consuming.

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