Coded excitation schemes with encoded pulse delays enhanced ultrasound blood flow imaging signal quality by over 7 dB without sacrificing resolution or framerate.
Coded excitation schemes and deep learning deconvolution can significantly enhance signal quality and resolution in ultrasound-based blood flow imaging.
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Ultrasound-based blood flow imaging has emerged as a key technology for early and personalized diagnosis. Vector flow imaging, in particular, enables flow field quantification at sub-millimeter spatial resolution and millisecond temporal resolution. While the approach has a demonstrated clinical potential, it remains hindered by, on the one hand, signal quality and signal-to-noise ratio and, on the other hand, imaging resolution. Here, we will discuss some recent advances made toward improving flow quantification. Signal quality can be enhanced by several dBs by using coded excitation schemes such as cascaded waves that can be further increased to an excess of 7 dB by encoding the delays between the pulses. Furthermore, this approach can be achieved without loss of resolution or framerate. On the resolution end, we will discuss how these enhanced signals can be used in deep learning-based deconvolution strategies for super-resolution imaging, and validated in preliminary experiments. These results pave the road toward high-accuracy, high-resolution, and high-speed ultrasound-based blood flow imaging.
Lajoinie et al. (Wed,) reported a other. Coded excitation schemes with encoded pulse delays enhanced ultrasound blood flow imaging signal quality by over 7 dB without sacrificing resolution or framerate.