Ultrafast Power Doppler Imaging Using Frame-Multiply-and-Sum-Based Nonlinear Compounding

Ultrafast power Doppler imaging based on coherent compounding (UPDI-CC) has become a promising technique for microvascular imaging due to its high sensitivity to slow blood flows. However, since this method utilizes a limited number of plane-wave or diverging-wave transmissions for high-frame-rate imaging, it suffers from degraded image quality because of the low contrast resolution. In this article, an ultrafast power Doppler imaging method based on a nonlinear compounding framework, called frame-multiply-and-sum (UPDI-FMAS), is proposed to improve contrast resolution. In UPDI-FMAS, unlike conventional channel-domain delay-multiply-and-sum (DMAS) beamforming, the signal coherence is estimated based on autocorrelation function over plane-wave angle frames. To avoid phase distortion of blood flow signals during the autocorrelation process, clutter filtering is preferentially applied to individual beamformed plane-wave data set. Therefore, only coherent blood flow signals are emphasized, while incoherent background noise is suppressed. The performance of the UPDI-FMAS was evaluated with simulation, phantom, and in vivo studies. For the simulation and phantom studies with a constant laminar flow, the UPDI-FMAS showed improvements in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) to those of UPDI-CC, i.e., over 10 and 7 dB for 13 plane waves, respectively, and the performances were improved as the number of plane waves increased. Moreover, the enhancement of the image quality due to the increased SNR and CNR in UPDI-FMAS was more clearly depicted with the in vivo study, in which a human kidney and a tumor-bearing mouse were evaluated. These results indicate that the FMAS compounding can improve the image quality of UPDI for microvascular imaging without loss of temporal resolution.

Automated summary

UPDI-FMAS is a promising technique for microvascular imaging based on a nonlinear compounding framework, which improves contrast resolution by estimating signal coherence using autocorrelation function and emphasizing coherent blood flow signals through clutter filtering. The method shows improvements in signal-to-noise ratio and contrast-to-noise ratio compared to conventional UPDI-CC, especially with the increase in the number of plane waves, leading to enhanced image quality in in vivo studies.