There areseveral ways to emulate ultrasound class of simulators, usually designed fortraining purposes, calculation of segments is based on the use of tomography recordingfor anatomy and ray tracing to simulate wave propagation. Reporting inEffective Results 2 – 4, both regarding image quality and simulation time.However, while being able toinfluence the model such as reversible and shading, rock pattern often notphysically 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-ModeScanning, goal from simulated beam to beam will vary slightly, based on a motionmodel. Simulated ultrasound data have range of applications. Automatic segmentationalgorithms used for quantitative image analysis, have number of free parametersthat must be tuned in order to achieve maximum performance for the specific applications.Fast simulation on ultrasound image is not only have importance for educationalpurpose but also for validation and standardization of existing techniques 1.
I. Introduction Keywords – Simulation,Ultrasonic imaging.Abstract – Simulated ultrasound data is an importanttool for the development and validation of quantitative image analysis methodsin echocardiography.
Unfortunately, simulation time can be prohibitive forlarge number of scatters to be included for scripts. The COLE algorithm by GAOet al is a fast Convolution-based simulator that performs simulation accuracyfor better speed. We offer GPU implementation of highly customizable CPU andCPU algorithm with an emphasis on dynamic simulation, which includes movingpoint scatters. We argue that it is important to reduce the amount of datatransfer from the CPU to get good performance on the GPU. We receive this asthe spline curve in the GPU memory as storage of complete trajectories of thisdynamic 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 thatefficiently eliminates the fickle artifacts visible in B-mode scenes, when CLEis used without adequate temporary oversampling. In order to assess theperformance, we used a laptop computer and a desktop computer, each with amulticore Intel CPU and an NVIDIA GPU. Run the simulator on a high-end Titan XGPU, we saw two commands of magnitude speedup compared to the parallel CPUversion, compared to the time of simulation performed by Gao et al in threeorders of magnitude in his paper on Cole, and 27,000 times faster than themultithreaded version of Field Two, using the numbers given in a letter byJensen.
We hope that by releasing the simulator as an open-source project, wewill use it and encourage further development.