[Comparison of wall filter algorithms for ultrasonic microvascular imaging]

Area of Science:

• Medical Imaging
• Ultrasound Technology
• Biomedical Engineering

Background:

• The resolution of blood flow imaging in ultrasonic microvascular imaging is significantly influenced by the design of the wall filter.
• Traditional wall filter algorithms may have limitations in effectively separating microvascular signals from noise and tissue.
• Advancements in signal processing are crucial for improving the clarity and diagnostic capabilities of ultrasound.

Purpose of the Study:

• To compare the performance of a traditional polynomial regression wall filter with two singular value decomposition (SVD)-based algorithms: Full-SVD and RS-RSVD.
• To evaluate the effectiveness of these algorithms in extracting micro blood flow signals.
• To assess the efficiency and suitability of the RS-RSVD algorithm for real-time, high frame rate microvascular imaging.

Main Methods:

• Experimental comparison using simulated data and human renal entity data.
• Implementation and testing of polynomial regression, Full-SVD, and RS-RSVD wall filter algorithms.
• Analysis of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), alongside execution time measurements.

Main Results:

• The traditional polynomial regression wall filter demonstrated limited filtering effects.
• Both Full-SVD and RS-RSVD algorithms effectively extracted micro blood flow signals from tissue and noise.
• RS-RSVD, when randomly divided into 16 blocks, achieved comparable SNR to Full-SVD while reducing execution time by 90.41% and CNR by 2.05 dB.

Conclusions:

• RS-RSVD algorithm offers superior performance in microvascular imaging compared to traditional methods.
• The RS-RSVD algorithm significantly improves operational efficiency, making it suitable for real-time, high frame rate ultrasound applications.
• This advancement facilitates better visualization and analysis of microvascular structures in clinical settings.