A simplified and accelerated implementation of SVD for filtering ultrafast power Doppler images.

BACKGROUND AND OBJECTIVE: Ultrafast Power Doppler (UPD) is a growing ultrasound modality for imaging and diagnosing microvasculature disease. A key element of UPD is using singular value decomposition (SVD) as a highly selective filter for tissue and electronic noise. However, two significant drawbacks of SVD are its computational burden and the complexity of its algorithms. These limitations hinder the development of fast and specific SVD algorithms for UPD imaging. This study introduces power SVD (pSVD), a simplified and accelerated algorithm for filtering tissue and noise in UPD images. METHODS: pSVD exploits several mathematical properties of SVD specific to UPD images. In particular, pSVD allows the direct computation of blood-related SVD components from the temporal singular vectors. This feature simplifies the expression of SVD while significantly accelerating its computation. After detailing the theory behind pSVD, we evaluate its performances in several in vitro and in vivo experiments and compare it to SVD and randomized SVD (rSVD). RESULTS: pSVD strongly decreases the running time of SVD (between 5 and 12 times in vivo) without impacting the quality of UPD images. Compared to rSVD, pSVD can be significantly faster (up to 3 times) or slightly slower but gives access to more estimators to isolate tissue subspaces. CONCLUSION: pSVD is highly valuable for implementing UPD imaging in clinical ultrasound and provides a better understanding of SVD for ultrasound imaging in general.

Adaptive noise reduction for power Doppler imaging using SVD filtering in the channel domain and coherence weighting of pixels.

Objective. Ultrafast power Doppler (UPD) is an ultrasound method that can image blood flow at several thousands of frames per second. In particular, the high number of data provided by UPD enables the use of singular value decomposition (SVD) as a clutter filter for suppressing tissue signal. Notably, is has been demonstrated in various applications that SVD filtering increases significantly the sensitivity of UPD to microvascular flows. However, UPD is subjected to significant depth-dependent electronic noise and an optimal denoising approach is still being sought.Approach. In this study, we propose a new denoising method for UPD imaging: the Coherence Factor Mask (CFM). This filter is first based on filtering the ultrasound time-delayed data using SVD in the channel domain to remove clutter signal. Then, a spatiotemporal coherence mask that exploits coherence information between channels for identifying noisy pixels is computed. The mask is finally applied to beamformed images to decrease electronic noise before forming the power Doppler image. We describe theoretically how to filter channel data using a single SVD. Then, we evaluate the efficiency of the CFM filter for denoisingin vitroandin vivoimages and compare its performances with standard UPD and with three existing denoising approaches.Main results. The CFM filter gives gains in signal-to-noise ratio and contrast-to-noise ratio of up to 22 dB and 20 dB, respectively, compared to standard UPD and globally outperforms existing methods for reducing electronic noise. Furthermore, the CFM filter has the advantage over existing approaches of being adaptive and highly efficient while not requiring a cut-off for discriminating noise and blood signals nor for determining an optimal coherence lag.Significance. The CFM filter has the potential to help establish UPD as a powerful modality for imaging microvascular flows.

Flow Rate and Low Hematocrit Measurements for In Vitro Blood Processing With Doppler Ultrasound.

In vitro techniques for the processing of flowing blood and its components have recently emerged from microfluidics. The blood flow rate and hematocrit are two keys parameters to monitor for guaranteeing the reliability of these techniques. But, there is a lack of monitoring methods adapted to low flow rates and small tubing. In this study, we exploit minimization approaches of continuous Doppler measurements to survey the blood flow rate. Combined with a packing factor model, we also estimate hematocrit from the Doppler spectrum. The presented method is implemented with a continuous-wave (CW) Doppler probe mounted on a 3D-printed support. The accuracy of the flow rate was measured in the range from 0.5 to 1.5 mL/min. For each of four different blood bags, hematocrit in the range under 8% was estimated from the Doppler spectrum using a packing factor model derived from the other three bags. Flow rate estimation shows a mean measurement error under 3% for a measurement time of 2 s. The mean error is still under 5% for a measurement time of 0.5 s. Hematocrit estimation for the four blood bags shows errors of 1.4%, 1.1%, 0.67%, and 0.70% Hct for a measurement time of 5 s. The versatility and simplicity of the method make it highly valuable for in vitro blood processing, in particular for low hematocrit blood fractionation techniques derived from microfluidics, as it can be performed through sterile tubing.