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Ultrasound transmission tomography image reconstruction with a fully convolutional neural network

Zhao, Wenzhao; Wang, Hongjian; Gemmeke, Hartmut 1; Dongen, Koen W. A. van; Hopp, Torsten ORCID iD icon 1; Hesser, Jürgen
1 Institut für Prozessdatenverarbeitung und Elektronik (IPE), Karlsruher Institut für Technologie (KIT)


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. ... mehr

DOI: 10.1088/1361-6560/abb5c3
Zitationen: 14
Web of Science
Zitationen: 10
Zitationen: 12
Zugehörige Institution(en) am KIT Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1361-6560
KITopen-ID: 1000127749
HGF-Programm 54.02.02 (POF III, LK 01) Ultraschnelle Datenauswertung
Erschienen in Physics in medicine and biology
Verlag Institute of Physics Publishing Ltd (IOP Publishing Ltd)
Band 65
Heft 23
Seiten Article: 235021
Vorab online veröffentlicht am 27.11.2020
Nachgewiesen in Dimensions
Web of Science
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