Independent component analysis-based tissue clutter filtering for plane wave perfusion ultrasound imaging

Non-contrast perfusion ultrasound imaging is difficult, mainly because of tissue clutter interference with blood. We previously developed an adaptive tissue clutter demodulation technique to overcome this problem and showed that power Doppler image quality can be improved when combining adaptive demodulation with improvements in beamforming and tissue filtering, namely angled plane wave beamforming and singular value decomposition filtering. In this work we aim to evaluate an independent component analysis-based filtering method using angled plane wave beamforming and compare it to singular value decomposition filtering with and without adaptive demodulation using single vessel simulations and phantoms. We show that with optimal filter cutoffs, independent component analysis-based filtering consistently improves signal and contrast-to-noise ratios, and it resulted in an 8.4dB average increase in optimal signal-to-noise ratio compared to singular value decomposition filtering in phantoms with 1mm/s flow and a 700ms ensemble.