scholarly journals H-scan Subtraction Doppler Imaging: A Novel Ultrasound Small Blood Vessel Flow Characterization with Scattering and Reflection Identification

2020 ◽  
Vol 10 (21) ◽  
pp. 7604
Author(s):  
Yang Jiao ◽  
Derong Zhang ◽  
Yiwen Xu ◽  
Yang Chen ◽  
Zhe Wu ◽  
...  

Ultrafast compound Doppler imaging (UCDI), which can be used to acquire Doppler information at very high spatial and temporal sampling rates, provides a great improvement to the characterization of the vasculature. The singular value decomposition (SVD) technique takes advantage of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters in small animals. However, a major challenge of conventional UCDI with SVD clutter filtering for small vessel imaging is that it is not sensitive enough to detect the hemodynamic changes in deep tissue where the majority of the remaining signal is usually noise-saturated. In this study, with the first attempt to apply ultrasonic tissue characterization techniques to UCDI, we propose an H-scan subtraction Doppler imaging method to bypass the limitations associated with the high-order singular value thresholding selection and improve the image quality of fine vessels. The flow phantom experiments with different blood concentrations show that H-Scan is capable of estimating the relative size and spatial distribution of acoustic scattering objects. In the in vivo rabbit brain experiment, the H-Doppler method, together with the global and block-wise local SVD clutter filtering, are proposed to facilitate better power Doppler images with a significant improvement of background noise suppression. These results demonstrate that the contrast-to-noise-ratio (CNR) of the H-scan subtraction Doppler imaging is 15% to 65% higher than that of the conventional UCDI methods. Therefore, this approach can be potentially applied to the clinical applications of the functional ultrasound (fUS) imaging method.

2009 ◽  
Vol 181 (4S) ◽  
pp. 101-101
Author(s):  
Michael L Eisenberg ◽  
Janet E Cowan ◽  
Peter R. Carroll ◽  
Katsuto Shinohara

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