Improving the Computational Complexity of Artificial Ultrasound Imaging Using a Combination of Independent Component Analysis and Adaptive Filter
Purpose: The artificial aperture imaging method owns a good contrast in the data recording and imaging process. However, this method is very time consuming that prevents its practical implementation.
Materials and Methods: In this paper, the separated waveforms are sent by two elements together, instead of a single element, and the combination of the methods of independent component analysis and adaptive filtering both are used to extract different components in the received echoes. The obtained result illustrates that the imaging is performed in less time, and the computational complexity of this method is declined.
Results and Conclusion: The proposed algorithm has been evaluated on two sets of simulated data and experimental data. The results indicate that the proposed method in the point phantom mode is only 1.5% worse in the resolution than the conventional artificial aperture method. Also, from the contrasting viewpoint, the proposed method has made the CR parameter worse by about 1.34dB than the conventional artificial aperture method. These adverse points of resolution and contrast in the proposed method are neglected than the conventional artificial aperture method because of a slight decrease in image quality than the artificial aperture method. However, the proposed method improves the computational complexity by 45% than the conventional artificial aperture method. As a result, it has brought the researchers closer to the practical implementation of artificial aperture imaging.
|Issue||Vol 7 No 4 (2020)|
|Medical Ultrasound Imaging Computational Complexity Artificial Aperture Method Independent Component Analysis Adaptive Filter|
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