Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

Journal Article (2020)
Author(s)

Wenzhao Zhao (University of Heidelberg)

Hongjian Wang (Donghua University)

Hartmut Gemmeke (Karlsruhe Institut für Technologie)

Koen W.A.Van Dongen (TU Delft - ImPhys/Medical Imaging)

Torsten Hopp (Karlsruhe Institut für Technologie)

Jurgen Hesser (University of Heidelberg)

Research Group
ImPhys/Medical Imaging
Copyright
© 2020 Wenzhao Zhao, Hongjian Wang, Hartmut Gemmeke, K.W.A. van Dongen, Torsten Hopp, Jürgen Hesser
DOI related publication
https://doi.org/10.1088/1361-6560/abb5c3
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Wenzhao Zhao, Hongjian Wang, Hartmut Gemmeke, K.W.A. van Dongen, Torsten Hopp, Jürgen Hesser
Research Group
ImPhys/Medical Imaging
Issue number
23
Volume number
65
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Abstract

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.

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