An inverse method using Cross spectral Matrix Fitting for passive cavitation imaging.

High intensity focused ultrasound (HIFU) can produce cavitation, which requires monitoring for specific applications such as sonoporation, targeted drug delivery or histotripsy. Passive acoustic mapping has been proposed in the literature as a method for monitoring cavitation, but it lacks spatial resolution, primarily in the axial direction, due to the absence of a time reference. This is a common issue with passive imaging compared to standard pulse-echo ultrasound. In order to improve the axial resolution, we propose an adaptation of the Cross spectral Matrix Fitting (CMF) method for passive cavitation imaging, which is based on the resolution of an inverse problem with different regularizations that promote sparsity in the reconstructed cavitation maps: Elastic Net (CMF-ElNet) and sparse Total Variation (CMF-spTV). The results from both simulated and experimental data are presented and compared to state-of-the-art approaches, such as the frequential Delay-and-Sum (DAS) and the frequential Robust Capon Beamformer (RCB). We show the interest of the method for improving the axial resolution, with an axial Full Width Half Maximum (FWHM) divided by 3 and 5 compared to RCB and DAS, respectively. Moreover, CMF based methods improve Contrast-to-Noise Ratio (CNR) by more than 15 dB in experimental conditions compared to RCB. We also show the advantage of the sparse Total Variation prior over Elastic Net when dealing with cloud shaped cavitation sources, that can be assumed as sparse grouped sources.

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.