Keywords: contrast-enhanced ultrasound, deconvolution, dilated convolutional neural network, microbubbles, super-resolution
TL;DR: We present a super-resolution ultrasound approach based on direct deconvolution of unprocessed ultrasound RF data with a dilated CNN
Abstract: We present a super-resolution ultrasound approach based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). Data are generated with a physics-based simulator that simulates the echoes from a dense cloud of monodisperse microbubbles and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved ultrasound data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.
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Paper Type: recently published or submitted journal contributions
Primary Subject Area: Image Acquisition and Reconstruction
Secondary Subject Area: Detection and Diagnosis
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