ABSTRACT
Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD's sensitivity to threshold selection, along with the RPCA's limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.
ABSTRACT
Fractional flow has been proposed for quantifying the degree of functional stenosis in cerebral arteries. Herein, subharmonic aided pressure estimation (SHAPE) combined with plane wave (PW) transmission was employed to noninvasively estimate the pressure distribution and fractional flow in the middle cerebral artery (MCA) in vitro. Consequently, the effects of incident sound pressure (peak negative pressures of 86-653 kPa), pulse repetition frequency (PRF), number of pulses, and blood flow rate on the subharmonic pressure relationship were investigated. The radio frequency data were stored and beamformed offline, and the subharmonic amplitude over a 0.4 MHz bandwidth was extracted using a 12-cycle PW at 4 MHz. The optimal incident sound pressure was 217 kPa without skull (sensitivity = 0.09 dB/mmHg; r2 = 0.997) and 410 kPa with skull (median sensitivity = 0.06 dB/mmHg; median r2 = 0.981). The optimal PRF was 500 Hz, as this value affords the highest sensitivity (0.09 dB/mmHg; r2 = 0.976) and temporal resolution. In addition, the blood flow rate exhibited a lesser effect on the subharmonic pressure relationship in our experimental setup. Using the optimized parameters, the blood pressure distribution and fractional flow (FFs) were measured. As such, the FFs value was in high agreement with the value measured using the pressure sensor (FFm). The mean ± standard deviations of the FF difference (FFm - FFs) were 0.03 ± 0.06 without skull and 0.01 ± 0.05 with skull.
