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\begin{document}

\title{Enhancement of Ultrasound Microbubble and Blood Flow Imaging using
Similarity Measurement}



\author[1]{Chengwu Huang }%
\affil[1]{Mayo Clinic College of Medicine and Science}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
Recent advancements in ultrasound technologies, such as ultrasound
localization microscopy and ultrafast ultrasound Doppler, have enabled
high-definition imaging of microvasculature. However, detecting weak
microbubble or blood flow signals amid strong background noise remains a
challenge, particularly in deep tissues. This study aims to enhance the
signal contrast of microbubble and blood flow by leveraging their
distinct spatial-temporal coherence in comparison to undesired noise for
robust microbubble detection and microvascular imaging. We propose to
quantify the signal coherence based on similarity analysis of beamformed
ultrasound microbubble/blood flow data within the plane wave imaging
framework. A spatial pixel is considered more likely to be a true
microbubble/blood flow signal with a higher level of similarity, which
can be measured by either of the following methods: 1) spatially
block-wise normalized cross-correlation between two compounded frames;
2) temporally normalized autocorrelation across multiple compounded
frames; 3) normalized cross-correlation between two subsets of
post-compounded frames; 4) normalized autocorrelation of the
pre-compounded data across angular direction. The original
microbubble/blood flow signal is then weighted by the similarity
measurement on a pixel-by-pixel basis to generate images with an
improved signal contrast. The robustness of the proposed methods was
first demonstrated in both phantom experiments and in vivo microbubble
data from kidney transplant. We further validated their feasibility in
blood flow imaging without the use of microbubbles based on in vivo data
of human liver and kidney. Significant contrast improvement was
observed, facilitating better visualization and detection of both
microbubble and noncontrast microflow signals, which indicates a great
potential of the methods for improved microvascular imaging and
widespread clinical translation.%
\end{abstract}%



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