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Adaptive Virtual Source Imaging Using the Sequence Intensity Factor: Simulation and Experimental Study

Chichao Zheng, Yazhong Wang, Yadan Wang*, Qing He, Hu Peng

Hefei University of Technology, Hefei, 230009, China

* Corresponding Author: Yadan Wang. Email: email

(This article belongs to the Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)

Computer Modeling in Engineering & Sciences 2022, 130(1), 397-413. https://doi.org/10.32604/cmes.2022.016308

Abstract

Virtual source (VS) imaging has been proposed to improve image resolution in medical ultrasound imaging. However, VS obtains a limited contrast due to the non-adaptive delay-and-sum (DAS) beamforming. To improve the image contrast and provide an enhanced resolution, adaptive weighting algorithms were applied in VS imaging. In this paper, we proposed an adjustable generalized coherence factor (aGCF) for the synthetic aperture sequential beamforming (SASB) of VS imaging to improve image quality. The value of aGCF is adjusted by a sequence intensity factor (SIF) that is defined as the ratio between the effective low resolution scan lines (LRLs) intensity and total LRLs strength. The aGCF-weighted VS (aGCF-VS) images were compared with standard VS images and GCF-weighted VS (GCF-VS) images. Simulation and experimental results demonstrated that the contrast ratio (CR) and contrast-to-noise ratio (CNR) of aGCF-VS are greatly improved, compared with standard VS imaging. And in comparison with GCF-VS, aGCF-VS can obtain better CNR and speckle signal-to-noise ratio (sSNR) while maintaining similar CR. Therefore, aGCF is suitable for VS imaging to improve contrast and preserve speckle pattern.

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APA Style
Zheng, C., Wang, Y., Wang, Y., He, Q., Peng, H. (2022). Adaptive virtual source imaging using the sequence intensity factor: simulation and experimental study. Computer Modeling in Engineering & Sciences, 130(1), 397-413. https://doi.org/10.32604/cmes.2022.016308
Vancouver Style
Zheng C, Wang Y, Wang Y, He Q, Peng H. Adaptive virtual source imaging using the sequence intensity factor: simulation and experimental study. Comput Model Eng Sci. 2022;130(1):397-413 https://doi.org/10.32604/cmes.2022.016308
IEEE Style
C. Zheng, Y. Wang, Y. Wang, Q. He, and H. Peng "Adaptive Virtual Source Imaging Using the Sequence Intensity Factor: Simulation and Experimental Study," Comput. Model. Eng. Sci., vol. 130, no. 1, pp. 397-413. 2022. https://doi.org/10.32604/cmes.2022.016308



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