- 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.
- Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.
- Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
- Paper Type: recently published or submitted journal contributions
- Primary Subject Area: Image Acquisition and Reconstruction
- Secondary Subject Area: Detection and Diagnosis
- Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.