The blood flow is traditionally obtained by multiplying the cross sectional area of the blood vessel and the average blood speed in the cross section, or is given by the integral of the product of the cross section and blood velocity of each element. However, both methods are affected greatly by the measurement precision of the area and velocity. A new algorithm, which is based on the Doppler blood flow spectrogram, is proposed to measure the blood flow in this paper. In the algorithm, the blood flow is calculated according to the double integral of a Doppler blood flow spectrogram. To verify the feasibility of the proposed algorithm, experiments have been performed on the Doppler blood-mimicking system KS205D−1 using the SonixTouch ultrasonic system. In addition, linear-regression analysis is carried out to observe the correlation factors between the experimental values and real values of different flow rates. Experimental results show that the calculated values and real values correlate significantly (r > 0.969, P < 0.0000001). Experimental results both on males and females also verified the proposed algorithm (r > 0.915, P < 0.00053). Hence the proposed algorithm is proven effective for relative mean blood flow measurement. Due to the special structure of the human brain, it is difficult to measure the cross sectional area of blood vessel with ultrasound imaging. In this algorithm, there is no need to measure the cross sectional area of the blood vessel. Therefore, the proposed algorithm has the potential to be a new method for clinical ultrasonic blood flow measurement, especially cerebral blood flow measurement. INDEX TERMS Blood flow measurement, Doppler spectrogram, linear-regression analysis, Doppler blood-mimicking system.
The high frame rate (HFR) imaging technique requires only one emission event for imaging. Therefore, it can achieve ultrafast imaging with frame rates up to the kHz regime, which satisfies the frame rate requirements for imaging moving tissues in scientific research and clinics. Lu's Fourier migration method is based on a nondiffraction beam to obtain HFR images and can improve computational speed and efficiency. However, in order to obtain high-quality images, Fourier migration needs to make full use of the spectrum of echo signals for imaging, which requires a large number of Fast Fourier Transform (FFT) points and increases the complexity of the hardware when the echo frequency is high. Here, an efficient algorithm using the spectrum migration technique based on the spectrum's distribution characteristics is proposed to improve the imaging efficiency in HFR imaging. Since the actual echo signal spectrum is of limited bandwidth, low-frequency and high-frequency parts with low-energy have little contribution to the imaging spectrum. We transform the effective part that provides the main energy in the signal spectrum to the imaging spectrum while the ineffective spectrum components are not utilized for imaging. This can significantly reduce the number of Fourier transform points, improve Fourier imaging efficiency, and ensure the imaging quality. The proposed method is evaluated on simulated and experimental datasets. Results demonstrated that the proposed method could achieve equivalent image quality with a reduced point number for FFT compared to the complete spectrum migration. In this paper, it only requires a quarter of the FFT points used in the complete spectrum migration, which can improve the computational efficiency; thus, it is more suitable for real-time data processing. The proposed spectrum migration method has a specific significance for the study and clinical application of HFR imaging.
Coherent plane-wave compounding (CPWC) is widely used in medical ultrasound imaging, in which plane-waves tilted at multiple angles are used to reconstruct ultrasound images. CPWC helps to achieve a balance between frame rate and image quality. However, the image quality of CPWC is limited due to sidelobes and noise interferences. Filtering techniques and adaptive beamforming methods are commonly used to suppress noise and sidelobes. Here, we propose a neighborhood singular value decomposition (NSVD) filter to obtain high-quality images in CPWC. The NSVD filter is applied to adaptive beamforming by combining with adaptive weighting factors. The NSVD filter is advantageous because of its singular value decomposition (SVD) and smoothing filters, performing the SVD processing in neighboring regions while using a sliding rectangular window to filter the entire imaging region. We also tested the application of NSVD in adaptive beamforming. The NSVD filter was combined with short-lag spatial coherence (SLSC), coherence factor (CF), and generalized coherence factor (GCF) to enhance performances of adaptive beamforming methods. The proposed methods were evaluated using simulated and experimental datasets. We found that NSVD can suppress noise and achieve improved contrast (contrast ratio (CR), contrast-to-noise ratio (CNR) and generalized CNR (gCNR)) compared to CPWC. When the NSVD filter is used, adaptive weighting methods provide higher CR, CNR, gCNR and speckle signal-to-noise ratio (sSNR), indicating that NSVD is able to improve the imaging performance of adaptive beamforming in noise suppression and speckle pattern preservation.
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