|W. Simson, R. Göbl, M. Paschali, M. Krönke, K. Scheidhauer, S. Weber, N. Navab
End-to-End Learning-Based Ultrasound Reconstruction
The first two authors contributed equally.
The original manuscript is available currently online on arXiv. (bib)
|Ultrasound imaging is caught between the quest for the highest image quality, and the necessity for clinical usability. Our contribution is two-fold: First, we propose a novel fully convolutional neural network for ultrasound reconstruction. Second, a custom loss function tailored to the modality is employed for end-to-end training of the network. We demonstrate that training a network to map time-delayed raw data to a minimum variance ground truth offers performance increases in a clinical environment. In doing so, a path is explored towards improved clinically viable ultrasound reconstruction. The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning. A clinical evaluation is conducted to verify the diagnostic usefulness of the proposed method in a clinical setting.|
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