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 subsample delay technique that
efficiently eliminates the fickle artifacts visible in B-mode scenes, when COLE
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.

                                                                                             
I.      Introduction

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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 subsample delay technique that
efficiently eliminates the fickle artifacts visible in B-mode scenes, when COLE
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.