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Non-contrast perfusion ultrasound imaging remains challenging due to spectral broadening of the tissue clutter signal caused by patient and sonographer hand motion. To address this problem, we previously introduced an adaptive demodulation scheme to suppress the bandwidth of tissue prior to high-pass filtering. Our initial implementation used single plane wave power Doppler imaging and a conventional tissue filter. Recent advancements in beamforming and tissue filtering have been proposed for improved slow-flow imaging, including coherent flow power Doppler (CFPD) imaging and singular value decomposition (SVD) filtering. Here, we aim to evaluate adaptive demodulation in conjunction with improvements in beamforming and filtering using simulations, single vessel phantoms, and an in vivo liver tumor embolization study. We show that simulated blood-to-background contrast-to-noise ratios are highest when using adaptive demodulation with CFPD and a 100ms ensemble, which resulted in a 13.6dB average increase in contrast-to-noise ratio compared to basic IIR filtering alone. We also show that combining adaptive demodulation with SVD and with CFPD+SVD results in 9.3dB and 19dB increases in contrast-to-noise ratios compared to IIR filtering alone at 700ms and 500ms ensembles for phantom data with 1mm/s and 5mm/s average flows, respectively. In general, combining techniques resulted in higher signal-to-noise, contrast-to-noise, and generalized contrast-to-noise ratios in both simulations and phantoms. Finally, adaptive demodulation with SVD resulted in the largest qualitative and quantitative change in tumor-tobackground contrast post-embolization.
This article was published in the following journal.
Name: IEEE transactions on ultrasonics, ferroelectrics, and frequency control
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