ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2021
Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Recent advances in photoacoustic-ultrasound dual-modality fusion imaging for functional visualization of the superficial tumor microenvironment.Oncology reviews · 2026Review
- Model-informed deep-learning photoacoustic reconstruction for low-element linear array.Photoacoustics · 2025Article
- Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor beamforming.Biomedical optics express · 2024Article
- Review
- Improving Minimum Variance Beamforming with Sub-Aperture Processing for Photoacoustic Imaging.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition.Journal of biomedical optics · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS
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