Low-complexity generalized coherence factor estimated from binarized signals in ultrasound beamforming

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: In coherence-based beamforming (CBB) using a generalized coherence factor (GCF), unnecessary signals caused by sidelobes are reduced, and an excellent contrast-to-noise ratio (CNR) is achieved in ultrasound imaging. However, the GCF computation is complex compared to the standard delay-and-sum (DAS) beamforming. In the present study, we propose a method that significantly reduces the number of GCF computations. Methods: In the previously proposed GCFreal, generation of the analytic signal for each element in the conventional GCF could be omitted. Furthermore, in GCF estimated from binarized signals (GCFB) proposed in the present study, the GCF value is calculated after the received signal of each element is binarized to reduce the computational complexity of the GCF. Results: The values of GCFB and GCFreal estimated from simulation and experimental data were compared. We also evaluated the image quality of B-mode images weighted by GCFB and GCFreal. Compared with GCFreal, GCFB was superior in reducing unnecessary signals but tended to reduce the brightness of the diffused scattering media. The CNR improvement was comparable for both methods. Conclusion: Generalized coherence factor estimated from binarized signals exhibits excellent CNR improvement compared to DAS. CNR improvements yielded by GCFB and GCFreal may depend on the observation target; however, under the conditions of the present study, comparable performances were obtained. Because GCFB can significantly reduce the computational complexity, it is potentially applicable in clinical diagnostic equipment.

Original languageEnglish
Pages (from-to)259-272
Number of pages14
JournalJournal of Medical Ultrasonics
Volume48
Issue number3
DOIs
Publication statusPublished - 2021 Jul

Keywords

  • Adaptive beamforming
  • Generalized coherence factor
  • Ultrasound imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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