# Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time

arXiv: https://arxiv.org/abs/2005.04931 · HTML: https://mlsanity.com/paper/2005.04931/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 1 of 1)

- [Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training](https://mlsanity.com/paper/2101.08339/) (2021)

## Abstract (arXiv)

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.