Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques

Navchetan Awasthi (Corresponding author), Laslo van Anrooij, Gino Jansen, Hans Martin Schwab, Josien P.W. Pluim, Richard G.P. Lopata

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review


Ultrasound (US) imaging is a medical imaging modality that uses the reflection of sound in the range of 2–18 MHz to image internal body structures. In US, the frequency bandwidth (BW) is directly associated with image resolution. BW is a property of the transducer and more bandwidth comes at a higher cost. Thus, methods that can transform strongly bandlimited ultrasound data into broadband data are essential. In this work, we propose a deep learning (DL) technique to improve the image quality for a given bandwidth by learning features provided by broadband data of the same field of view. Therefore, the performance of several DL architectures and conventional state-of-the-art techniques for image quality improvement and artifact removal have been compared on in vitro US datasets. Two training losses have been utilized on three different architectures: a super resolution convolutional neural network (SRCNN), U-Net, and a residual encoder decoder network (REDNet) architecture. The models have been trained to transform low-bandwidth image reconstructions to high-bandwidth image reconstructions, to reduce the artifacts, and make the reconstructions visually more attractive. Experiments were performed for 20%, 40%, and 60% fractional bandwidth on the original images and showed that the improvements obtained are as high as 45.5% in RMSE, and 3.85 dB in PSNR, in datasets with a 20% bandwidth limitation.

Originele taal-2Engels
Aantal pagina's19
TijdschriftHealthcare (Switzerland)
Nummer van het tijdschrift1
StatusGepubliceerd - jan. 2023

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© 2022 by the authors.


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