Effective Adaptive Dynamic Quadrature Demodulation in Medical Ultrasound Imaging

  • Yoon, Heechul (Digital Media & Communications R&D Center, Samsung Electronics Co. Ltd) ;
  • Jeon, Kang-won (Digital Media & Communications R&D Center, Samsung Electronics Co. Ltd) ;
  • Lee, Hyuntaek (Digital Media & Communications R&D Center, Samsung Electronics Co. Ltd) ;
  • Kim, Kyeongsoon (Dept. of Pharmaceutical Engineering, Inje University) ;
  • Yoon, Changhan (Dept. of Biomedical Engineering, Inje University)
  • Received : 2016.11.08
  • Accepted : 2017.09.26
  • Published : 2018.01.01


In medical ultrasound imaging, frequency-dependent attenuation downshifts and reduces a center frequency and a frequency bandwidth of received echo signals, respectively. This causes considerable errors in quadrature demodulation (QDM), result in lowering signal-to-noise ratio (SNR) and contrast resolution (CR). To address this problem, adaptive dynamic QDM (ADQDM) that estimates center frequencies along depth was introduced. However, the ADQDM often fails when imaging regions contain hypoechoic regions. In this paper, we introduce a valid region-based ADQDM (VR-ADQDM) method to reject the misestimated center frequencies to further improve SNR and CR. The valid regions are regions where the center frequency decreases monotonically along depth. In addition, as a low-pass filter of QDM, Gaussian wavelet based dynamic filtering was adopted. From the phantom experiments, average SNR improvements of the ADQDM and the VR-ADQDM over the traditional QDM were 1.22 and 5.27 dB, respectively, and the corresponding maximum SNR improvements were 2.56 and 10.58 dB. The contrast resolution of the VR-ADQDM was also improved by 0.68 compared to that of the ADQDM. Similar results were obtained from in vivo experiments. These results indicate that the proposed method would offer promises for imaging technically-difficult patients due to its capability in improving SNR and CR.

E1EEFQ_2018_v13n1_468_f0001.png 이미지

Fig. 1. (a) Overall block diagram of proposed adaptivedynamic quadrature demodulation (VR-ADQDM)and (b) flow chart of center frequency estimationmethod in the proposed method

E1EEFQ_2018_v13n1_468_f0002.png 이미지

Fig. 2. Estimation results of (a) center frequencies and (b)valid regions, respectively. These representativeresults are obtained from a scanline indicated with awhite line in Fig. 4(a)

E1EEFQ_2018_v13n1_468_f0003.png 이미지

Fig. 3. Results of center frequency estimation: (a) Estimatedcenter frequencies along with extracted valuesbased on the valid regions and (b) final curve ofreconstructed center frequencies along imagingdepth

E1EEFQ_2018_v13n1_468_f0004.png 이미지

Fig. 4. Fetal phantom images by (a) CQDM, (b) ADQDM,(c) proposed VR-ADQDM methods and (d) its validregion map

E1EEFQ_2018_v13n1_468_f0005.png 이미지

Fig. 5. Estimated center frequencies as a function of depthand reconstructed curve by conventional ADQDMand proposed VR-ADQDM

E1EEFQ_2018_v13n1_468_f0006.png 이미지

Fig. 6. SNR improvement produced by the conventionalADQDM and proposed VR-ADQDM over CQDM

E1EEFQ_2018_v13n1_468_f0007.png 이미지

Fig. 7. Phantom images by (a) CQDM, (b) ADQDM, (c)proposed VR-ADQDM and (d) its valid region map

E1EEFQ_2018_v13n1_468_f0008.png 이미지

Fig. 8. Estimated center frequencies and reconstructedcurves by the conventional ADQDM and proposedmethod

E1EEFQ_2018_v13n1_468_f0009.png 이미지

Fig. 9. Improvement of (a) SNR and (b) CR produced bythe conventional ADQDM and proposed VR-ADQDM over CQDM

E1EEFQ_2018_v13n1_468_f0010.png 이미지

Fig. 10. In vivo liver images by (a) CQDM, (b) ADQDM,(c) proposed VR-ADQDM and (d) its valid regionmap

E1EEFQ_2018_v13n1_468_f0011.png 이미지

Fig. 11. (a) Estimated center frequencies and reconstructedcurve by conventional ADQDM and proposed VR-ADQDM and (b) SNR improvement of eachmethod over the conventional QDM


Supported by : Inje University


  1. J. H. Chang, J. T. Yen and K. K. Shung, "A novel envelope detection for high-frame rate, high-frequency ultrasound imaging," IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 54, no, 9, pp. 1792-1801, 2007.
  2. G.-D. Kim, C. Yoon, S. -B. Kye, Y. Lee, J. Kang, Y. Yoo and T. -K. Song, "A single FPGA-based portable ultrasound imaging system for point-of-care applications," IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 59, no. 7, pp. 1386-1394, 2012.
  3. J. Kang, C. Yoon, J. Lee, S. Kye, Y. Lee, J. H. Chang, G. Kim, Y. Yoo and T.-K. Song, "A system-on-chip solution for point-of-care ultrasound imaging systems: Architecture and ASIC implementation," IEEE Trans. Biomed. Circuits Syst., vol. 10, no. 2, pp. 412-423, 2016.
  4. T. Baldweck, P. Laugier, A. Herment and G. Berger, "Application of autoregressive spectral analysis for ultrasound attenuation estimation: interest in highly attenuating medium," IEEE Trans. Ultrason. Ferroelectr. Freq. Control., vol. 42, no. 1, pp. 99-110, 1995.
  5. J. A. Jensen, Estimation of Blood Velocities using Ultrasound: A Signal Processing Approach, Cambridge University Press, Cambridge, UK, 1996, pp. 29-191.
  6. S. D. Pye, S. R. Wild and W. N. McDicken, "Adaptive time gain compensation for ultrasonic imaging," Ultrasound Med. Biol., vol. 18, no. 2, pp. 205-212, 1992.
  7. J. Girault, F. Ossant, A. Ouahabi, D. Kouname and F. Patat, "Time-varying autoregressive spectral estimation for ultrasound attenuation in tissue characterization," IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., vol. 45, no. 3, pp. 650-659, 1998.
  8. D.Y. Lee, Y. Yoo, T.K. Song and J.H. Chang, "Adaptive dynamic quadrature demodulation with autoregressive spectral estimation in ultrasound imaging," Biomed. Signal Proces., vol. 7, no. 4, pp. 371-378, 2012.
  9. N. Feng, J. Zhang and W. Wang, "A quadrature demodulation method based on tracking the ultrasound echo frequency," Ultrasonics, vol. 44, pp. e47-e50, 2006.
  10. C. Yoon, G. -D. Kim, Y. Yoo, T. -K. Song and J. H. Chang, "Frequency equalized compounding for effective speckle reduction in medical ultrasound imaging," Biomed. Signal Proces., vol. 8, no. 6, pp. 876-887, 2013.
  11. G. Park, S. Yeo, J. J. Lee, C. Yoon, H. -W, Koh, H. Lim, Y. Kim, H. Shim and Y. Yoo, "New adaptive clutter rejection based on spectral analysis for ultrasound color Doppler imaging: Phantom and in vivo abdominal study," IEEE Trans. Biomed. Eng., vol. 61, no. 1, pp. 55-63, 2014.
  12. M. Fink, F. Hottier and J. F. Cardoso, "Ultrasonic signal processing for in vivo attenuation measurement: Short time Fourier analysis," Ultrason. Imaging, vol. 5, no. 2, pp. 117-135, 1983.
  13. M.O. Culjat, D. Goldenberg, P. Tewari and R.S. Singh, "A review of tissue substitutes for ultrasound imaging," Ultrasound Med. Biol., vol. 36, no. 6, pp. 861-873, 2010.
  14. P. Wang, Y. Shen and Q. Wang, "Gaussian wavelet based dynamic filtering (GWDF) method for medical ultrasound systems," Ultrasonics, vol. 46, no. 2, pp. 168-176, 2007.
  15. K.F. Ustuner and G.L. Holley, Ultrasound imaging system performance assessment, AAPM 45th Annual Meeting, San Diego, CA, 2003, pp. 10-14.
  16. C. Yoon, Y. Lee, J. H. Chang, T. -K. Song and Y. Yoo, "In vitro estimation of mean sound speed based on minimum average phase variance in medical ultrasound imaging," Ultrasonics, vol. 51, no. 7, pp. 795-802, 2011.