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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

Author(s): Guangzhi Dai, Zhiyong He* and Hongwei Sun

Volume 16, Issue 3, 2020

Page: [262 - 272] Pages: 11

DOI: 10.2174/1573405615666191209151746

open_access

Abstract

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems.

Objectives: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis.

Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation.

Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent.

Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

Keywords: Block compressed sensing, ultrasonic imaging, sparse representation, wavelet transformation, phased array, image reconstruction.

Graphical Abstract
[1]
Candes EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006; 52(2): 489-509.
[http://dx.doi.org/10.1109/TIT.2005.862083]
[2]
Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006; 52(4): 1289-306.
[http://dx.doi.org/10.1109/TIT.2006.871582]
[3]
Kirolos S, Laska J, Wakin M, et al. Analog-to-information conversion via random demodulation. IEEE Dallas/CAS workshop on design, applications, integration and software 2006; 71-4.
[4]
Zelinski AC, Wald LL, Setsompop K, Goyal VK, Adalsteinsson E. Sparsity-enforced slice-selective MRI RF excitation pulse design. IEEE Trans Med Imaging 2008; 27(9): 1213-29.
[http://dx.doi.org/10.1109/TMI.2008.920605] [PMID: 18779063]
[5]
Willett RM, Gehm ME, Brady DJ. Multiscale reconstruction for computational spectral imaging. Computational Imaging 2007; 6498: 64980L-64980L-15.
[http://dx.doi.org/10.1117/12.715711]
[6]
Sheikh M, Milenkovic O, Dai W, Baraniuk G. Compressive sensing DNA microbarrays. Rice University Technical Report ECE-07- 06 2007; 1-8.
[7]
Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58(6): 1182-95.
[http://dx.doi.org/10.1002/mrm.21391] [PMID: 17969013]
[8]
Willet R. Compressed Sensing for practical optical imaging systems: A tutorial. Opt Eng 2011; 50(7): 586.
[9]
Robucci R, Gray JD, Chiu LK, et al. Compressive sensing on a CMOS separable-transform image sensor. Proc IEEE 2010; 98(6): 1089-101.
[http://dx.doi.org/10.1109/JPROC.2010.2041422]
[10]
Wagadarikar A, John R, Willett R, Brady D. Single disperser design for coded aperture snapshot spectral imaging. Appl Opt 2008; 47(10): B44-51.
[http://dx.doi.org/10.1364/AO.47.000B44] [PMID: 18382550]
[11]
Gan L. Block compressed sensing of natural images. In: 15th International Conference on Digital Signal Processing. IEEE: Cardiff, UK 2007; pp. 403-6.
[12]
Ke J, Lam EY. Object reconstruction in block-based compressive imaging. Opt Express 2012; 20(20): 22102-17.
[http://dx.doi.org/10.1364/OE.20.022102] [PMID: 23037360]
[13]
Zheng L, Maleli A, Liu Q, et al. An lp-based reconstruction algorithm for compressed sensing radar imaging.IEEE Radar Conference (RadarConf). IEEE: Philadelphia, PA 2016; pp. 1-5.
[http://dx.doi.org/10.1109/RADAR.2016.7485202]
[14]
Xi Y, Zhao J, Bennett JR, Stacy MR, Sinusas AJ, Wang G. Simultaneous CT-MRI reconstruction for constrained imaging geometries using structural coupling and compressive sensing. IEEE Trans Biomed Eng 2016; 63(6): 1301-9.
[http://dx.doi.org/10.1109/TBME.2015.2487779] [PMID: 26672028]
[15]
Sheng-Liang L, Kun L, Feng Z, et al. Innovative remote sensing imaging method based on compressed sensing. Opt Laser Technol 2014; 63: 83-9.
[http://dx.doi.org/10.1016/j.optlastec.2014.03.019]
[16]
Golub MA, Averbuch A, Nathan M, et al. Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser. Appl Opt 2016; 55(3): 432-43.
[http://dx.doi.org/10.1364/AO.55.000432] [PMID: 26835914]
[17]
Ning L, Setsompop K, Michailovich O, et al. A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. Neuroimage 2016; 125: 386-400.
[http://dx.doi.org/10.1016/j.neuroimage.2015.10.061] [PMID: 26505296]
[18]
Friboulet D, Liebgott H, Prost R. Compressive sensing for raw RF signals reconstruction in ultrasound.International ultrasonics symposium. IEEE. San Diego, CA, USA 2010; pp. 367-70.
[http://dx.doi.org/10.1109/ULTSYM.2010.5935766]
[19]
Quinsac C, Basarab A, Kouamé D. Frequency domain compressive sampling for ultrasound imaging. Adv Acoust Vib 2012; 2012: 1-16.
[http://dx.doi.org/10.1155/2012/231317]
[20]
Liebgott H, Prost R, Friboulet D. Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing. Ultrasonics 2013; 53(2): 525-33.
[http://dx.doi.org/10.1016/j.ultras.2012.09.008] [PMID: 23089222]
[21]
Dobigeon N, Basarab A, Kouamé D. Regularized Bayesian compressed sensing in ultrasound imaging.Proceedings of the 20th European Signal Processing Conference (EUSIPCO). IEEE: Bucharest, Romania 2012.
[22]
Quinsac C, Vieilleville FD, Basarab A, et al. Compressed sensing of ultrasound single-orthant analytical signals. International ultrasonics symposium, IEEE. Orlando, FL, USA 2011; pp. 1419-22.
[http://dx.doi.org/10.1109/ULTSYM.2011.0351]
[23]
Quinsac C, Basarab A, Kouame D, et al. 3D compressed sensing ultrasound Imaging.Ultrasonics Symposium (IUS). IEEE 2010; pp. 363-6.
[24]
Quinsac C, Basarab A, Girault J, et al. Compressed sensing of ultrasound images: Samplin g of spatial and frequency domains Signal Processing Systems (SIPS). IEEE 2010; pp. 231-6.
[http://dx.doi.org/10.1109/SIPS.2010.5624793]
[25]
Baron D, Duarte MF, Sarvotham S, et al. An information-theoretic approach to distributed compressed sensing. Proceedings of 43rd Conference on Communication, Control, and Computing 2005.
[26]
Basarab A, Liebgott H, Bernard O, et al. Medical ultrasound image reconstruction using distributed compressive sampling. In: 10th International symposium on biomedical imaging. San Francisco, CA, USA 2013; pp. 628-31.
[http://dx.doi.org/10.1109/ISBI.2013.6556553]
[27]
Mishali M, Eldar YC, Dounaevsky O, Shoshan E. Xampling: Analog to digital at sub-nyquist rates. IET Circuits Dev Syst 2011; 5(1): 8-20.
[http://dx.doi.org/10.1049/iet-cds.2010.0147]
[28]
Michaeli T, Eldar YC. Xampling at the rate of innovation. IEEE Trans Signal Process 2012; 60(3): 1121-33.
[http://dx.doi.org/10.1109/TSP.2011.2178409]
[29]
Wagner N, Eldar YC, Friedman Z. Compressed beamforming in ultrasound imaging. IEEE Trans Signal Process 2012; 60(9): 4643-57.
[http://dx.doi.org/10.1109/TSP.2012.2200891]
[30]
Chernyakova T, Eldar Y. Fourier-domain beamforming: the path to compressed ultrasound imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2014; 61(8): 1252-67.
[http://dx.doi.org/10.1109/TUFFC.2014.3032] [PMID: 25073133]
[31]
Cohen R, Eldar YC. Sparse convolutional beamforming for ultrasound imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2018; 65(12): 2390-406.
[http://dx.doi.org/10.1109/TUFFC.2018.2874256] [PMID: 30296220]
[32]
Bar-Zion A, Tremblay-Darveau C, Solomon O, Adam D, Eldar YC. Fast vascular ultrasound imaging with enhanced spatial resolution and background rejection. IEEE Trans Med Imaging 2017; 36(1): 169-80.
[http://dx.doi.org/10.1109/TMI.2016.2600372] [PMID: 27541629]
[33]
Burshtein A, Birk M, Chernyakova T, Eilam A, Kempinski A, Eldar YC. Sub-nyquist sampling and fourier domain beamforming in volumetric ultrasound imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2016; 63(5): 703-16.
[http://dx.doi.org/10.1109/TUFFC.2016.2535280] [PMID: 26930678]
[34]
Bar-Zion A, Solomon O, Tremblay-Darveau C, Adam D, Eldar YC. SUSHI: Sparsity-Based Ultrasound Super-Resolution Hemodynamic Imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2018; 65(12): 2365-80.
[http://dx.doi.org/10.1109/TUFFC.2018.2873380] [PMID: 30295619]
[35]
Lahav A, Chernyakova T, Eldar YC. FoCUS: Fourier-based coded ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control 2017; 64(12): 1828-39.
[http://dx.doi.org/10.1109/TUFFC.2017.2760359] [PMID: 28991738]
[36]
Vetterli M, Marziliano P, Blu T. Sampling signals with finite rate of innovation. IEEE Trans Signal Process 2002; 50(6): 1417-28.
[http://dx.doi.org/10.1109/TSP.2002.1003065]
[37]
Tur R, Eldar YC, Friedman Z. Innovation rate sampling of pulse streams with application to ultrasound imaging. IEEE Trans Signal Process 2011; 59(4): 1827-42.
[http://dx.doi.org/10.1109/TSP.2011.2105480]
[38]
Gedalyahu K, Tur R, Eldar YC. Multichannel sampling of pulse streams at the rate of innovation. IEEE Trans Signal Process 2011; 59(4): 1491-504.
[http://dx.doi.org/10.1109/TSP.2011.2105481]
[39]
Shi G, Chen C, Lin J, Xie X, Chen X. Narrowband ultrasonic detection with high range resolution: separating echoes via compressed sensing and singular value decomposition. IEEE Trans Ultrason Ferroelectr Freq Control 2012; 59(10): 2237-53.
[http://dx.doi.org/10.1109/TUFFC.2012.2449] [PMID: 23143573]
[40]
Yi Lv. Study on ultrasonic imaging technology of sparse medicine. Institute of Acoustics, Chinese Academy of Sciences Beijing: China 2013.

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