There quantitative image analysis, have number of free parameters

There are
several ways to emulate ultrasound class of simulators, usually designed for
training purposes, calculation of segments is based on the use of tomography recording
for anatomy and ray tracing to simulate wave propagation. Reporting in
Effective Results 2 – 4, both regarding image quality and simulation time.
However, while being able to
influence the model such as reversible and shading, rock pattern often not
physically accurate enough, e.g., for Doppler Simulation.

Dynamic simulations are important in the heart and pulse imaging,
for B-mode and Doppler imaging. While simulating Color Doppler or M-Mode
Scanning, goal from simulated beam to beam will vary slightly, based on a motion

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Simulated ultrasound data have range of applications. Automatic segmentation
algorithms used for quantitative image analysis, have number of free parameters
that must be tuned in order to achieve maximum performance for the specific applications.
Fast simulation on ultrasound image is not only have importance for educational
purpose but also for validation and standardization of existing techniques 1.

I.      Introduction

Keywords – Simulation,
Ultrasonic imaging.

Abstract – Simulated ultrasound data is an important
tool for the development and validation of quantitative image analysis methods
in echocardiography. Unfortunately, simulation time can be prohibitive for
large number of scatters to be included for scripts. The COLE algorithm by GAO
et al is a fast Convolution-based simulator that performs simulation accuracy
for better speed. We offer GPU implementation of highly customizable CPU and
CPU algorithm with an emphasis on dynamic simulation, which includes moving
point scatters. We argue that it is important to reduce the amount of data
transfer from the CPU to get good performance on the GPU. We receive this as
the spline curve in the GPU memory as storage of complete trajectories of this
dynamic point scatters. It leads to good efficiency, when large card frames,
such as B-mode and tissue Doppler data, index for the whole cardiac cycle.
Apart from this, we propose a phase-based Anuradha delay technique that
efficiently eliminates the fickle artifacts visible in B-mode scenes, when CLE
is used without adequate temporary oversampling. In order to assess the
performance, we used a laptop computer and a desktop computer, each with a
multicore Intel CPU and an NVIDIA GPU. Run the simulator on a high-end Titan X
GPU, we saw two commands of magnitude speedup compared to the parallel CPU
version, compared to the time of simulation performed by Gao et al in three
orders of magnitude in his paper on Cole, and 27,000 times faster than the
multithreaded version of Field Two, using the numbers given in a letter by
Jensen. We hope that by releasing the simulator as an open-source project, we
will use it and encourage further development.


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