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 denoising in vitro and in vivo images 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.
Meniscal tear in the knee joint is a highly common injury that can require an ablation. However, the success rate of meniscectomy is highly impacted by difficulties in estimating the thin vascularization of the meniscus, which determines the healing capacities of the patient. Indeed, the vascularization is estimated using arthroscopic cameras that lack of a high sensitivity to blood flow. Here, we propose an ultrasound method for estimating the density of vascularization in the meniscus during surgery. This approach uses an arthroscopic probe driven by ultrafast sequences. To enhance the sensitivity of the method, we propose to use a chirp-coded excitation combined to a mismatched compression filter robust to the attenuation. This chirp approach was compared to a standard ultrafast emission and a Hadamard-coded emission using a flow phantom. The mismatched filter was also compared to a matched filter. Results show that, for a velocity of a few mm.s -1 , the mismatched filter gives a 4.4 to 10.4 dB increase of the signal-to-noise ratio compared to the Hadamard emission and a 3.1 to 6.6 dB increase compared to the matched filter. Such increases are obtained for a loss of axial resolution of 13% when comparing the point spread functions of the mismatched and matched filters. Hence, the mismatched filter allows increasing significantly the probe capacity to detect slow flows at the cost of a small loss in axial resolution. This preliminary study is the first step toward an ultrasensitive ultrasound arthroscopic probe able to assist the surgeon during meniscectomy.
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