Application of low-complexity generalized coherence factor to in vivo data

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Purpose: Beamforming using the generalized coherence factor (GCF) reduces sidelobe artifacts and provides an excellent contrast-to-noise ratio. We previously proposed GCFreal, a method to calculate GCF without generating analytic signals, and GCFbin, a method to calculate GCF by binarizing the received signals. In this study, we applied these methods to in vivo data and showed the effect of the computational complexity reduction on contrast performance. Methods: Channel RF data were acquired from the human liver and gallbladder. We set up several observation points in each data set and investigated the mechanism that causes the differences in contrast performance among the methods based on the signals and their power spectra in the channel direction. Results: For GCF and GCFreal, the obtained values were almost the same. However, there were large differences in GCFbin from GCF when the signals from the focus point or from outside the focus point were received on different channels. This is because the amplitudes of the signals with high coherence and those with low coherence were changed by binarizing the signals. Conclusion: While GCFbin can significantly reduce the computational complexity, there are differences in the values of GCFbin and GCF due to binarizing of the received signals. However, this difference resulted in GCFbin being superior to GCF in terms of artifact reduction. This is owing to the elimination of amplitude information in GCFbin, which makes it a new efficient coherence factor with different characteristics from GCF.

Original languageEnglish
Pages (from-to)555-567
Number of pages13
JournalJournal of medical ultrasonics (2001)
Issue number4
Publication statusPublished - 2022 Oct


  • Adaptive beamforming
  • Generalized coherence factor
  • In vivo data
  • Ultrasound imaging


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