Background: Doppler ultrasound is an important diagnostic tools used to
view blood flow through the vessels. For the reconstruction we need to collect a
large data to get a Doppler image with high performance.
Objective: In this work, we propose an algorithm that combines compressed sensing
(CS) and parallel computing to reconstruct the Doppler ultrasound signal and reduce
the reconstruction time.
Methods: Compressed sensing is a new sampling methods, appeared a few years
ago, but it was used in different practical applications such as speeding up MRI
scans by acquiring less data to achieve a given amount of resolution and Doppler ultrasound
signal reconstruction using fewer measurements to achieve an image with
high quality. The main idea of parallel signal/image reconstruction is to divide the
main tasks into subtasks and solve them concurrently, in such way that total time can be divided between
Results: The reconstruction performed using Matlab on a personal computer running a Windows 7
operating system. Real and simulated Doppler data were used for the proposed algorithm validation.
The result shows that as the number of cores increased the process time decreased. The image quality
from serial programming is same as that a achieved by using parallel programming. The best quality of
the image gain is when the 1-norm algorithm used. The lowest reconstruction time was obtained by
combining the regularized orthogonal matching pursuit (ROMP) algorithm and parallel computing, with
a reconstruction time less than 0.016 seconds when four cores were used and less than 0.034 seconds
when two cores were used for 5% of the data. When 80% of the data used the reconstruction time for
two cores was 0.109 and for four cores the time was 0.067. The best efficiency was achieved by
parallelizing ROMP in two and four cores and the lowest efficiency achieved by parallelized CoSaMP
algorithm. The excitation cost decreased with decreasing the numbers of cores. In all the reconstruction
algorithms used to perform this work, four cores gave a lower excitation cost than those obtained with
two cores. The lowest excitation cost was achieved via ROMP when four cores and fewer
measurements were used (0.06) and higher cost gained by using 1-norm algorithm (12.42). The ROMP
combined algorithm gives best result among all combined reconstruction algorithm used.
Conclusion: We have demonstrated that it is possible to combine compressed sensing and parallel
computing to reconstruct the Doppler ultrasound signal. The result of reconstructing the Doppler signal
shows that combined algorithm leads to a reduction in reconstruction time and gives an image in realtime
while reducing the computation complexity without affecting the image quality.