# Cardiac ultrasound simulation for autonomous ultrasound navigation

arXiv: https://arxiv.org/abs/2402.06463 · HTML: https://mlsanity.com/paper/2402.06463/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation](https://mlsanity.com/paper/2305.05424/) (2023)
- [Automatic Liver Segmentation Using an Adversarial Image-to-Image Network](https://mlsanity.com/paper/1707.08037/) (2017)
- [Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.13321/) (2020)
- [Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks](https://mlsanity.com/paper/1707.05392/) (2017)
- [Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images](https://mlsanity.com/paper/2103.05745/) (2021)
- [Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations](https://mlsanity.com/paper/2302.10698/) (2023)

## Built on by (most cited first, 4 of 4)

- [Goal-conditioned reinforcement learning for ultrasound navigation guidance](https://mlsanity.com/paper/2405.01409/) (2024)
- [UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation](https://mlsanity.com/paper/2501.05828/) (2025)
- [AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging](https://mlsanity.com/paper/2409.16898/) (2024)
- [UltraScatter: Ray-Based Simulation of Ultrasound Scattering](https://mlsanity.com/paper/2510.10612/) (2025)

## Abstract (arXiv)

Ultrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations. Thus, we propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images. We extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes. The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated.