Research on Reconstruction of Ultrasound Diffraction Tomography Based on Compressed Sensing_Journal of Biomedical Engineering

Authors：

HUAShaoyan ¹ , DINGMingyue ² ,  YUCHIMing ²

1. School of Information Engineering, Hubei University of Chinese Medicine, Wuhan 430065, China;
2. School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

Corresponding author：

YUCHIMing, Email: m.yuchi@gmail.com

Keywords：

compressed sensing; ultrasound diffraction tomography; image reconstruction; inverse problem

DOI：

10.7507/1001-5515.20150174

Video：

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Abstract Full text Figures/Tables Video References Cited by

Ultrasound diffraction tomography (UDT) possesses the characteristics of high resolution, sensitive to dense tissue, and has high application value in clinics. To suppress the artifact and improve the quality of reconstructed image, classical interpolation method needs to be improved by increasing the number of projections and channels, which will increase the scanning time and the complexity of the imaging system. In this study, we tried to accurately reconstruct the object from limited projection based on compressed sensing. Firstly, we illuminated the object from random angles with limited number of projections. Then we obtained spatial frequency samples through Fourier diffraction theory. Secondly, we formulated the inverse problem of UDT by exploring the sparsity of the object. Thirdly, we solved the inverse problem by conjugate gradient method to reconstruct the object. We accurately reconstructed the object using the proposed method. Not only can the proposed method save scanning time to reduce the distortion by respiratory movement, but also can reduce cost and complexity of the system. Compared to the interpolation method, our method can reduce the reconstruction error and improve the structural similarity.

Citation： HUAShaoyan, DINGMingyue, YUCHIMing. Research on Reconstruction of Ultrasound Diffraction Tomography Based on Compressed Sensing. Journal of Biomedical Engineering, 2015, 32(5): 975-982. doi: 10.7507/1001-5515.20150174 Copy

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16.	吕燚,吴文焘,李平.压缩感知在合成发射孔径医学超声成中的应用[J].声学学报, 2013, 38(4):426-1432.
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20.	CANDES E. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9-10):589-592.
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1. GREENLEAF J F,BAHN R C. Clinical imaging with transmissive ultrasonic Computerized tomography[J]. IEEE Transactions on Biomedical Engineering, 1981, 28(2):177-185.
2. HUTHWAITE P, SIMONETTI F, DURIC N. Combining time of flight and diffraction tomography for high resolution breast imaging:Initial in-vivo results (l)[J]. The Journal of the Acoustical Society of America, 2012, 132(3):1249-1252.
3. WATA K I, NAGATA R. Calculation of refractive index distribution from interferograms using the Born and Rytov's approximation[J]. Japanese Journal of Applied Physics, 1975, 14(1):379-384.
4. MUELLER R, KAVEH M, WADE G. Reconstructive tomography and applications to ultrasonics[J]. Proceedings of the IEEE, 1979, 67(4):567-587.
5. KAK A, SLANEY M. Principles of computerized tomographic imaging[M]. Philadelphia:Society of Industrial and Applied Mathematics, 2001:49-112.
6. SIMONETTI F, HUANG L, DURIC N. On the spatial sampling of wave fields with circular ring apertures[J]. Journal of Applied Physics, 2007, 101:083103.
7. CANDES E, ROMBERG J, TAO T. Robust uncertainty principles:Exact signalreconstruction from highly incomplete Fourier information[J]. IEEE Transactions on Information Theory, 2006,52(2):489-509.
8. DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
9. LUSTIG M, DONOHO D, PAULY J M. Sparse MRI:The application of compressed sensing for rapid MR imaging[J]. Magnetic Resonance in Medicine, 2008, 58(6):1182-1195.
10. SIDKY E Y, PAN X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization[J]. Physics in Medicine and Biology, 2008, 53(17):4777-4807.
11. WU D, LI L ZHANG L. Feature constrained compressed sensing CT image reconstruction from incomplete data via robust principal component analysis of the database[J]. Physics in Medicine and Biology,2013, 58(12):4047-4070.
12. CHINN G, OLCOTT P D, LEVIN C S. Sparse signal recovery methods for multiplexing pet detector readout[J]. IEEE Transactions on Medical Imaging, 2013, 32(5):932-942.
13. ZHANG Y, WANG Y, ZHANG C. Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction[J]. Ultrasonics, 2012, 52(8):1046-1055.
14. WANG G. Guest editorial compressive sensing for biomedical imaging[J]. Magnetic Resonance in Medicine, 2011, 30(5):1013-1016.
15. ZHANG Q, LI B, SHEN M. A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing:Initial development[J]. Ultrasonics, 2013, 53(1):255-264.
16. 吕燚,吴文焘,李平.压缩感知在合成发射孔径医学超声成中的应用[J].声学学报, 2013, 38(4):426-1432.
17. WAGNER N, ELDAR Y, FRIEDMAN Z. Compressed beamforming in ultrasound imaging[J]. IEEE Transactions on Signal Processing, 2012, 60(9):4643-4657.
18. LIEBGOTT H, PROST R, FRIBOULET D. Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing[J]. Ultrasonics, 2013, 53(2):525-533.
19. QUINSAC C, BASARAB A, KOUAME D. Frequency domain compressive sampling for ultrasound imaging[J]. Advances in Acoustics and Vibration, 2012, 12(6):1-16.
20. CANDES E. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9-10):589-592.
21. CANDES E, TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12):4203-4215.
22. CANDES E, ROMBERG J K, TAO T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8):1207-1223.
23. BRONSTEIN M M, BRONSTEIN A M, ZIBULEVSKY M, et al. Reconstruction in diffraction ultrasound tomography using non-uniform FFT[J]. IEEE Transactions on Medical Imaging, 2002, 21(11):1395-1401.

Journal of Biomedical Engineering

Research on Reconstruction of Ultrasound Diffraction Tomography Based on Compressed Sensing

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