Deep Network for Scatterer Distribution Estimation for Ultrasound Image
Simulation
- URL: http://arxiv.org/abs/2006.10166v1
- Date: Wed, 17 Jun 2020 21:25:13 GMT
- Title: Deep Network for Scatterer Distribution Estimation for Ultrasound Image
Simulation
- Authors: Lin Zhang, Valery Vishnevskiy, Orcun Goksel
- Abstract summary: We demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed ultrasound data.
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.
- Score: 8.13909718726358
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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