Key result
An adaptive singular value decomposition filtering strategy effectively suppressed hyperechoic moving structure artifacts in human carotid artery imaging compared to existing methods.
Why the study?
Applying power Doppler ultrasound to human arterial imaging remains challenging due to complex hyperechoic moving structures.
An adaptive SVD filtering strategy improves the suppression of hyperechoic moving structures in power Doppler ultrasound, potentially enhancing 3-D imaging of human blood vessels.
The power Doppler (PD) ultrasound imaging provides high-quality, noninvasive visualization of blood flow and has the potential to be used for circulation screening. However, its application in human arterial imaging remains challenging due to the presence of complex hyperechoic moving structures (HMSs). In this study, we propose an adaptive singular value decomposition (SVD) filtering strategy for HMS suppression. The proposed method used a k-means clustering algorithm directly on prebeamformed IQ data to segment HMS and non-HMS regions, followed by an adaptive SVD filtering strategy tailored to each tissue type. Compared to the existing SVD filtering methods, the proposed approach can effectively suppress the HMS artifacts. In addition, the human carotid artery imaging experiments demonstrate significant improvement in HMS suppression throughout cardiac cycles and across various imaging locations. With such capability, we believe that the proposed strategy will be a useful tool in applying PD for the 3-D imaging of human blood vessels.
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Wang et al. (2025) studied Human arterial imaging. Adaptive singular value decomposition (SVD) filtering strategy vs. Existing SVD filtering methods was evaluated on Hyperechoic moving structures (HMS) suppression. An adaptive singular value decomposition filtering strategy effectively suppressed hyperechoic moving structure artifacts in human carotid artery imaging compared to existing methods.
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