Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging.
Background: The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes...
| Publicado en: | BioMedical Engineering OnLine Vol. 21; no. 1; pp. 1 - 25 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
6/18/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157526737&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157526737 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 6/18/2022 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 157526737 157526737 NLM35717330 157526737 10.1186/s12938-022-01007-5 NLM35717330 157526737 ppf: 1 ppct: 24 formats: tig: atl: Minimum variance beamforming combined with covariance matrix-based adaptive weighting for medical ultrasound imaging. aug: au: Wang, Yuanguo Wang, Yadan Liu, Mingzhou Lan, Zhengfeng Zheng, Chichao Peng, Hu affil: School of Mechanical Engineering, Hefei University of Technology, 230009, Hefei, China sug: subj: Algorithms Signal Processing, Computer Assisted Phantoms, Imaging Ultrasonography Methods Image Processing, Computer Assisted Methods Computer Simulation Funding Source ab: Background: The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter.Methods: In this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation.Results: The proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer.Conclusion: Simulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of [Formula: see text] has the potential to be implemented in real time using the graphics processing unit. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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