High-Quality Plane Wave Compounding using Convolutional Neural Networks

Maxime Gasse 1 Fabien Millioz 1 Emmanuel Roux 2 Damien Garcia 2 Hervé Liebgott 2 Denis Friboulet 1
1 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
2 Imagerie Ultrasonore
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : Single plane wave (PW) imaging produces ultrasound (US) images of poor quality at high frame rates (ultrafast). High-quality PW imaging usually relies on the coherent compounding of several successive steered emissions (typically more than ten), which in turn results in a decreased frame rate. We propose a new strategy to reduce the number of emitted PWs by learning a compounding operation from data, i.e. by training a convolutional neural network (CNN) to reconstruct high quality images using a small number of transmissions. We present experimental evidence that this approach is promising, as we were able to produce high-quality images from only 3 PWs, competing in terms of contrast ratio and lateral resolution with the standard compounding of 31 PWs (10x speed-up factor).
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Maxime Gasse, Fabien Millioz, Emmanuel Roux, Damien Garcia, Hervé Liebgott, et al.. High-Quality Plane Wave Compounding using Convolutional Neural Networks. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Institute of Electrical and Electronics Engineers, 2017, 64 (10), pp.1637-1639. ⟨10.1109/TUFFC.2017.2736890⟩. ⟨hal-01596034⟩

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