We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a function that maps position and orientation of the transducer to ultrasound images. This is a first step towards realistic patient-specific simulations that will enable improved training and retrospective examination of complex cases. Our models can simulate a 2D image in under 4ms (well within real-time constraints), and produce simulated images that preserve the content (anatomical structures and artefacts) of real ultrasound images.
@article{arxiv.2005.04931,
title = {Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time},
author = {Cesare Magnetti and Veronika Zimmer and Nooshin Ghavami and Emily Skelton and Jacqueline Matthew and Karen Lloyd and Jo Hajnal and Julia A. Schnabel and Alberto Gomez},
journal= {arXiv preprint arXiv:2005.04931},
year = {2020}
}