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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

Abstract

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.

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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

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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)

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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

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

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

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

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

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

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

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

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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

Acknowledgement

Supported by : Inje University

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