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

Abstract

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.

RF Channel-Based Adaptive Beamforming for 3D Ultrasound Localization Microscopy

“…The coherence factor can exhibit significant discontinuities between adjacent pixels, impacting its effectiveness. One way of dealing with this effect is to calculate the coherence factor for all voxels and smooth it across the voxel dimension using a 3D Gaussian kernel 45 . This variation will be called CFGF (Coherence Factor with Gaussian Filtering) and evaluated as a separate beamformer from CF, as it might produce significant differences in the final ULM map, related to the spatial smoothing of the CF factor.…”

“…However, when using adaptive beamforming, it would be of greater importance to evaluate their effect on the microbubbles PSF before localization 23 . In fact, applying adaptive beamforming without tissue canceling can lead to a distortion of the signal of microbubbles 45 . Here, the SVD was performed on a spatiotemporal matrix containing the raw IQ signals recorded by all channels before any processing.…”

“…The value of p for pDAS varied from 2 to 4 and the number of iterations for iMAP from 1 to 4. The standard deviation for the Gaussian kernel in CFGF was fixed to one pixel as in 45 . The null voxels produced by the f-number were discarded to compute CF, CFGF, and iMAP.…”

Super-resolution reconstruction of ultrasound image using a modified diffusion model

“…Various techniques have been investigated to improve the image quality based on image post-processing methods. Most of these techniques have focused on the strategy of enhancing ultrasound image quality by reducing speckle noise (Pialot et al 2023, Yu et al 2023, improving the SNR (Sun et al 2023, Zou et al 2023, mitigating blurring artifacts (Govinahallisathyanarayana et al 2020, Khan et al 2020, etc. Another strategy is the super-resolution (SR) reconstruction technology, which is aimed to directly reconstruct high-resolution (HR) images from low-resolution (LR) ones (Wu et al 2019, Zhou et al 2019, Temiz and Bilge 2020.…”

Enhancement of Ultrasound Microbubble and Blood Flow Imaging using Similarity Measurement

“…This has resulted in significant image quality improvement in both B-mode ultrasound and blood flow images through beamformed data in multi-angle plane wave imaging [35,36]. More recently, the spatial coherence or a combination of spatial and angular coherence of the blood flow signal after SVD filtering in the channel domain, is leveraged to generate a pixel-wise coherence factor weighting map, which can be applied back to the blood flow signal to produce power Doppler image with substantial contrast improvement [37,38]. While channel data can offer a higher flexibility for signal handlings and denoising operations, these channel domain approaches are in general computationally intensive due to the significantly larger amount of channel data to be processed [35,37,38].…”