Spectral analysis framework for compressed sensing ultrasound signals
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Compressed sensing (CS) is the theory of the recovery of signals that are sampled below the Nyquist sampling rate. We propose a spectral analysis framework for CS data that does not require full reconstruction for extracting frequency characteristics of signals by an appropriate basis matrix.
The coefficients of a basis matrix already contain the spectral information for CS data, and the proposed framework directly utilizes them without completely restoring original data. We apply three basis matrices, i.e., DCT, DFT, and DWT, for sampling and reconstructing processes, subsequently estimating the attenuation coefficients to validate the proposed method. The estimation accuracy and precision, as well as the execution time, are compared using the reference phantom method (RPM).
The experiment results show the effective extraction of spectral information from CS signals by the proposed framework, and the DCT basis matrix provides the most accurate results while minimizing estimation variances. The execution time is also reduced compared with that of the traditional approach, which completely reconstructs the original data.
The proposed method provides accurate spectral analysis without full reconstruction. Since it effectively utilizes the data storage and reduces the processing time, it could be applied to small and portable ultrasound systems using the CS technique.
KeywordsCompressed sensing Ultrasound Attenuation coefficient Reference phantom method
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP); the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20174010201620); the National Research Foundation of Korea through the Ministry of Education and Ministry of Science (NRF-2017R1D1A1B03034733); and research grant of Kwangwoon University.
Compliance with ethical standards
Conflict of interest
We declare that we have no conflicts of interest in connection with this paper.
This article does not contain any studies with human or animal subjects performed by the any of the authors.
- 12.He P, Greenleaf JF. Application of stochastic-analysis to ultrasonic echoes—estimation of attenuation and tissue heterogeneity from peaks of echo envelope. J Ultrasound Med. 1986;79:526–34.Google Scholar
- 16.Fink M, Hottier F, Cardoso JF. Ultrasonic signal processing for in vivo attenuation measurement: short time Fourier analysis. Ultrason Imaging. 1983;5:117–35.Google Scholar
- 21.Liebgott H, Basarab A, Kouame D, et al. Compressive sensing in medical ultrasound. Proc IEEE Ultrason Symp. https://doi.org/10.1109/ULTSYM.2012.0486.
- 26.Nyquist H. Certain topics in telegraph transmission theory. AIEE Trans. 1928;47:617–44.Google Scholar