# Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation

arXiv: https://arxiv.org/abs/2006.10166 · HTML: https://mlsanity.com/paper/2006.10166/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging](https://mlsanity.com/paper/2301.10520/) (2023)
- [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)
- [An Ultra-Fast Method for Simulation of Realistic Ultrasound Images](https://mlsanity.com/paper/2109.10353/) (2021)
- [Robust Scatterer Number Density Segmentation of Ultrasound Images](https://mlsanity.com/paper/2201.06143/) (2022)
- [Deep Estimation of Speckle Statistics Parametric Images](https://mlsanity.com/paper/2206.04145/) (2022)

## Abstract (arXiv)

Simulation-based ultrasound training can be an essential educational tool. Realistic ultrasound image appearance with typical speckle texture can be modeled as convolution of a point spread function with point scatterers representing tissue microstructure. Such scatterer distribution, however, is in general not known and its estimation for a given tissue type is fundamentally an ill-posed inverse problem. In this paper, we demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed ultrasound data. We herein propose to impose a known statistical distribution on scatterers and learn the mapping between ultrasound image and distribution parameter map by training a convolutional neural network on synthetic images. In comparison with several existing approaches, we demonstrate in numerical simulations and with in-vivo images that the synthesized images from scatterer representations estimated with our approach closely match the observations with varying acquisition parameters such as compression and rotation of the imaged domain.