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The computing performance optimization of the Short-Lag Spatial Coherence (SLSC) method applied to ultrasound data processing is presented. The method is based on the theory that signals from adjacent receivers are correlated, drawing on a simplified conclusion of the van Cittert-Zernike theorem. It has been proven that it can be successfully used in ultrasound data reconstruction with despeckling. Former works have shown that the SLSC method in its original form has two main drawbacks: time-consuming processing and low contrast in the area near the transceivers. In this study, we introduce a method that allows to overcome both of these drawbacks. The presented approach removes the dependency on distance (the “lag” parameter value) between signals used to calculate correlations. The approach has been tested by comparing results obtained with the original SLSC algorithm on data acquired from tissue phantoms. The modified method proposed here leads to constant complexity, thus execution time is independent of the lag parameter value, instead of the linear complexity. The presented approach increases computation speed over 10 times in comparison to the base SLSC algorithm for a typical lag parameter value. The approach also improves the output image quality in shallow areas and does not decrease quality in deeper areas.
Wydawca
Czasopismo
Rocznik
Tom
Strony
669--679
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
autor
- The Faculty of Electronics and Information Technology, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland
autor
- The Faculty of Electronics and Information Technology, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland
autor
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
autor
- Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
Bibliografia
- 1. Bamber J. C., Mucci R. A., Orofino D. P. (2002), Spatial coherence and beamformer gain, [in:] Acoustical Imaging, Vol. 24, Springer Nature, pp. 43-48, doi: 10.1007/0-306-47108-6_6.
- 2. Benzarti F., Amiri H. (2012), Speckle noise reduction in medical ultrasound images, International Journal of Computer Science Issues, 9, 2, 3, 8 pages, https://arxiv.org/ftp/arxiv/papers/1305/1305.1344.pdf.
- 3. Bottenus N., Byram B. C., Dahl J. J., Trahey G. E. (2013), Synthetic aperture focusing for shortlag spatial coherence imaging, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 60, 9, 1816-1826, doi: 10.1109/tuffc.2013.2768.
- 4. Chen T. J., Chuang K. S., Wu J., Chen S. C., Hwang I. M., Jan M. L. (2003), A novel image quality index using Moran I statistics, Physics in Medicine and Biology, 48, 8, N131-N137, doi: 10.1088/0031-9155/48/8/402.
- 5. Dahl J. J., Lediju M. A., Trahey G. E. (2011), Methods, systems and apparatuses for van-Cittert Zernike imaging, US Patent 2013/0109971.
- 6. Gungor M. A., Karagoz I. (2015), The homogeneity map method for speckle reduction in diagnostic ultrasound images, Measurement, 68, 100-110, doi: 10.1016/j.measurement.2015.02.047.
- 7. Gupta S., Chauhan R. C., Sexana S. C. (2004), Wavelet-based statistical approach for speckle reduction in medical ultrasound images, Medical & Biological Engineering & Computing, 42, 2, 189-192, doi: 10.1007/bf02344630.
- 8. Hiremath P. S., Prema T., Badiger S. (2013), Speckle noise reduction in medical ultrasound images, [in:] Gunarathne G. P. P [Ed.], Advancements and break-throughs in ultrasound imaging, InTech, Chapter 8, pp. 201-241, doi: 10.5772/56519.
- 9. Hyun D., Trahey G. E., Dahl J. J. (2013), A GPU-based real-time spatial coherence imaging system, [in:] Proceedings of SPIE – Medical Imaging 2013: Ultrasonic Imaging, Tomography, and Therapy, J. G. Bosch, M. M. Doyley [Eds], Vol. 8675, doi: 10.1117/12.2008686.
- 10. Hyun D., Trahey G. E., Dahl J. J. (2014), Sparse sampling methods for efficient spatial coherence estimation, [in:] 2014 IEEE International Ultrasonics Symposium, pp. 535-538, Chicago, IL, doi: 10.1109/ultsym.2014.0132.
- 11. Hyun D., Trahey G. E., Dahl J. J. (2015), Real-time high-framerate in vivo cardiac SLSC imaging with GPU-based beamformer, [in:] 2015 IEEE International Ultrasonics Symposium (IUS), pp. 1-4, doi: 10.1109/ULTSYM.2015.0077.
- 12. Lediju M. A., Trahey G. E., Byram B. C., Dahl J. J. (2011), Short-lag spatial coherence of backscattered echoes: imaging characteristics, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 58, 7, 1377-1388, doi: 10.1109/tuffc.2011.1957.
- 13. Liu D. L., Waag R. C. (1995), About the application of the van Cittert-Zernike theorem in ultrasonic imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 42, 4, pp. 590-601, doi: 10.1109/58.393102.
- 14. Mallart R., Fink M. (1991), The van Cittert-Zernike theorem in pulse echo measurements, The Journal of the Acoustical Society of America, 90, 5, 2718-2727, doi: 10.1121/1.401867.
- 15. Matrone G., Savoia A. S., Caliano G., Magenes G. (2015), The delay multiply and sum beamforming algorithm in ultrasound B-mode medical imaging, IEEE Transactions on Medical Imaging, 34, 4, 940-949, doi: 10.1109/tmi.2014.2371235.
- 16. Ovireddy S., Muthusamy E. (2014), Speckle suppressing anisotropic diffusion filter for medical ultrasound images, Ultrasonic Imaging, 36, 2, 112-132. doi: 10.1177/0161734613512200.
- 17. Pourebrahimi B., Yoon S., Dopsa D., Kolios M. C. (2013), Improving the quality of photoacoustic images using the short-lag spatial coherence imaging technique, [in:] A. A. Oraevsky, L. V.Wang [Eds], Proceedings of the Photons Plus Ultrasound: Imaging and Sensing 2013, Vol. 8581, San Francisco, CA, doi: 10.1117/12.2005061.
- 18. S-Sharp (2019), Prodigy – Features, http://www.s-sharp.com/web/products/products.jsp?dm_id=DM1412758766647 (accessed on March 7, 2019).
- 19. Thompson, A. R., Moran J. M., Swenson G. W. (2017), Van Cittert-Zernike theorem, spatial coherence, and scattering, [in:] Interferometry and synthesis in radio astronomy, Springer International Publishing, Cham, pp. 767-786, doi: 10.1007/978-3-319-44431-4_15.
- 20. Trots I., Nowicki A., Lewandowski M., Tasinkevyvh Y. (2010), Multi-element synthetic transmit aperture in medical ultrasound imaging, Archives of Acoustics, 35, 4, 687-699, doi: 10.2478/v10168-010-0052-y.
- 21. Vanithamani R., Umamaheswari G. (2014), Speckle reduction in ultrasound images using neighshrink and bilateral filtering, Journal of Computer Science, 10, 4, 623-631, doi: 10.3844/jcssp.2014.623.631.
- 22. Verasonics (2019), Vantage – Research ultrasound, url: http://verasonics.com (accessed on March 7, 2019).
- 23. Walczak M., Lewandowski M., Zolek N. (2013), Optimization of real-time ultrasound PCIe data streaming and OpenCL processing for SAFT imaging, [in:] 2013 IEEE International Ultrasonics Symposium (IUS), Institute of Electrical and Electronics Engineers (IEEE), pp. 2064-2067, doi: 10.1109/ult-sym.2013.0527.
- 24. Wang Z., Bovik A. C., Sheikh H. R., Simoncelli E. P. (2004), Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13, 4, 600-612, doi: 10.1109/tip.2003.819861.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8fd200db-bea4-47b7-922d-434988b817d9