Singular value decomposition filtering improved contrast-to-noise ratio to 15.7 dB compared to -0.5 dB with conventional filtering, and reduced standard deviation of cardiac blood flow.
Does singular value decomposition (SVD) improve contrast-to-noise ratio and reduce standard deviation in high-frame-rate cardiac blood flow imaging compared to conventional filtering?
Singular value decomposition-based signal processing improves image quality and reduces noise in high-frame-rate cardiac blood flow imaging.
Absolute Event Rate: 15.7% vs -0.5%
Abstract Visualization of blood flow is important to evaluate the cardiac function. In this study, we propose two signal processing techniques based on singular value decomposition (SVD) for the visualization of blood flow: one is a filtering for clutter reduction and the other is a regularization method for color Doppler images. In the clutter filtering, contrast-to-noise ratio obtained by the SVD filtering was better (15.7 dB) than that by conventional finite impulse response filtering (−0.5 dB). In color flow imaging, the standard deviation of cardiac blood flow was decreased from 0.022 to 0.014 mm s −1 by the proposed regularization method.
Mozumi et al. (Tue,) conducted a other in Cardiac blood flow imaging. Singular value decomposition (SVD) based signal processing techniques vs. Conventional finite impulse response filtering was evaluated on Contrast-to-noise ratio for clutter reduction. Singular value decomposition filtering improved contrast-to-noise ratio to 15.7 dB compared to -0.5 dB with conventional filtering, and reduced standard deviation of cardiac blood flow.