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.

Single-pixel image reconstruction from experimental data using neural networks.

Single-pixel cameras that measure image coefficients have various promising applications, in particular for hyper-spectral imaging. Here, we investigate deep neural networks that when fed with experimental data can output high-quality images in real time. Assuming that the measurements are corrupted by mixed Poisson-Gaussian noise, we propose to map the raw data from the measurement domain to the image domain based on a Tikhonov regularization. This step can be implemented as the first layer of a deep neural network, followed by any architecture of layers that acts in the image domain. We also describe a framework for training the network in the presence of noise. In particular, our approach includes an estimation of the image intensity and experimental parameters, together with a normalization scheme that allows varying noise levels to be handled during training and testing. Finally, we present results from simulations and experimental acquisitions with varying noise levels. Our approach yields images with improved peak signal-to-noise ratios, even for noise levels that were foreseen during the training of the networks, which makes the approach particularly suitable to deal with experimental data. Furthermore, while this approach focuses on single-pixel imaging, it can be adapted for other computational optics problems.