Clutter Mitigation in Echocardiography Using Sparse Signal Separation

[Comparison of wall filter algorithms for ultrasonic microvascular imaging]

The design of wall filter in ultrasonic microvascular imaging directly affects the resolution of blood flow imaging. We compared the traditional polynomial regression wall filter algorithm and two algorithms based on singular value decomposition (SVD), Full-SVD algorithm and RS-RSVD algorithm (random sampling based on random singular value decomposition) through experiments with simulated data and human renal entity data imaging experiments. The experimental results showed that the filtering effect of the traditional polynomial regression wall filter algorithm was limited, however, Full-SVD algorithm and RS-RSVD algorithm could better extract the micro blood flow signal from the tissue or noise signal. When RS-RSVD algorithm was randomly divided into 16 blocks, the signal-to-noise ratio was the same as that of Full-SVD algorithm, reduces the contrast-to-noise ratio by 2.05 dB, and reduces the execution time by 90.41%. RS-RSVD algorithm can improve the operation efficiency and is more conducive to the real-time imaging of high frame rate ultrasound microvessels.

Impact of skin tone on photoacoustic oximetry and tools to minimize bias

The major optical absorbers in tissue are melanin and oxy/deoxy-hemoglobin, but the impact of skin tone and pigmentation on biomedical optics is still not completely understood or adequately addressed. Melanin largely governs skin tone with higher melanin concentration in subjects with darker skin tones. Recently, there has been extensive debate on the bias of pulse oximeters when used with darker subjects. Photoacoustic (PA) imaging can measure oxygen saturation similarly as pulse oximeters and could have value in studying this bias. More importantly, it can deconvolute the signal from the skin and underlying tissue. Here, we studied the impact of skin tone on PA signal generation, depth penetration, and oximetry. Our results show that subjects with darker skin tones exhibit significantly higher PA signal at the skin surface, reduced penetration depth, and lower oxygen saturation compared to subjects with lighter skin tones. We then suggest a simple way to compensate for these signal differences.

A Spatial Near-Field Clutter Reduction Filter Preserving Tissue Speckle in Echocardiography

Near-field (NF) clutter in echocardiography is depicted as a diffuse haze hindering the visualization of the myocardium and the blood-pool, thereby degrading its diagnostic value. Several clutter filters have been developed, which are limited in patients with contraction motion and rhythm anomalies, and in 3-D ultrasound (US). This study introduces a new NF clutter reduction method, which preserves US speckles required for strain imaging. The filter developed detects the NF clutter region in the spatial frequency domain. The filter employs an oriented, multiscale approach, and assumes the NF clutter to be predominantly present in the highest and lowest bandpass images. These bandpass images were filtered, whilst sparing features in the myocardium and NF clutter-free regions. The performance of the filter was assessed in a volunteer study, in ten 3-D apical and parasternal view acquisitions, and in a retrospective clinical study composed of 20 cardiac patients with different indications for echocardiography. The filter reduced NF clutter in all data sets, whilst preserving all or most of the myocardium. Additionally, it demonstrated a consistent enhancement of image quality, with an increase in contrast of 4.3 dB on average, and generated a clearer myocardial boundary distinction. Furthermore, the speckles were preserved according to the quality index based on local variance, the structural similarity index method, and normalized cross correlation values, being 0.82, 0.92, and 0.95 on average, respectively. Global longitudinal strain measurements on NF clutter reduced images were improved or equivalent compared to the original acquisitions, with an average increase in strain signal-to-noise ratio of 34%.

Closed-Loop Low-Rank Echocardiographic Artifact Removal

Echocardiographic image sequences are frequently corrupted by quasi-static artifacts ("clutter") superimposed on the moving myocardium. Conventionally, localized blind source separation methods exploiting local correlation in the clutter have proven effective in the suppression of these artifacts. These methods use the spectral characteristics to distinguish the clutter from tissue and background noise and are applied exhaustively over the data set. The exhaustive application results in high computational complexity and a loss of useful tissue signal. In this article, we develop a closed-loop algorithm in which the clutter is first detected using an adaptively determined weighting function and then removed using low-rank estimation methods. We show that our method is adaptable to different low-rank estimators, by presenting two such estimators: sparse coding in the principal component domain and nuclear norm minimization. We compare the performance of our proposed method (CLEAR) with two methods: singular value filtering (SVF) and morphological component analysis (MCA). The performance was quantified in silico by measuring the error with respect to a known "ground truth" data set with no clutter for different combinations of moving clutter and tissue. Our method retains more tissue with a lower error of 3.88 ± 0.093 dB (sparse coding) and 3.47 ± 0.78 (nuclear norm) compared with the benchmark methods 8.5 ± 0.7 dB (SVF) and 9.3 ± 0.5 dB (MCA) particularly in instances where the rate of tissue motion and artifact motion is small (≤0.25 periods of center frequency per frame) while producing comparable clutter reduction performance. CLEAR was also validated in vivo by quantifying the tracking error over the cardiac cycle on five mouse heart data sets with synthetic clutter. CLEAR reduced the error by approximately 50%, compared with 25% for the SVF.

Blind Source Separation for Clutter and Noise Suppression in Ultrasound Imaging: Review for Different Applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

Robust Optical Flow Estimation in Cardiac Ultrasound Images Using a Sparse Representation

This paper introduces a robust 2-D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spatial smoothness and the sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced using robust weighting functions for the data fidelity term as well as for the spatial and sparse regularizations. The motion fields and the weights are computed jointly using an iteratively re-weighted minimization strategy. The proposed robust approach is evaluated on synthetic data and realistic simulation sequences with available ground-truth by comparing the performance with state-of-the-art algorithms. Finally, the proposed method is validated using two sequences of in vivo images. The obtained results show the interest of the proposed approach for 2-D cardiac ultrasound imaging.

Dictionary learning-based reverberation removal enables depth-resolved photoacoustic microscopy of cortical microvasculature in the mouse brain

Photoacoustic microscopy (PAM) capitalizes on the optical absorption of blood hemoglobin to enable label-free high-contrast imaging of the cerebral microvasculature in vivo. Although time-resolved ultrasonic detection equips PAM with depth-sectioning capability, most of the data at depths are often obscured by acoustic reverberant artifacts from superficial cortical layers and thus unusable. In this paper, we present a first-of-a-kind dictionary learning algorithm to remove the reverberant signal while preserving underlying microvascular anatomy. This algorithm was validated in vitro, using dyed beads embedded in an optically transparent polydimethylsiloxane phantom. Subsequently, we demonstrated in the live mouse brain that the algorithm can suppress reverberant artifacts by 21.0 ± 5.4 dB, enabling depth-resolved PAM up to 500 µm from the brain surface.

Accelerated Singular Value-Based Ultrasound Blood Flow Clutter Filtering With Randomized Singular Value Decomposition and Randomized Spatial Downsampling

Singular value decomposition (SVD)-based ultrasound blood flow clutter filters have recently demonstrated substantial improvement in clutter rejection for ultrafast plane wave microvessel imaging, and have become the commonly used clutter filtering method for many novel ultrafast imaging applications such as functional ultrasound and super-resolution imaging. At present, however, the computational burden of SVD remains as a major hurdle for practical implementation and clinical translation of this method. To address this challenge, in the study we present two blood flow clutter filtering methods based on randomized SVD (rSVD) and randomized spatial downsampling to accelerate SVD clutter filtering with minimal compromise to the clutter filter performance. rSVD accelerates SVD computation by approximating the k largest singular values, while random downsampling accelerates both full SVD and rSVD by decomposing the original large data matrix into small matrices that can be processed in parallel. An in vitro blood flow phantom study with the presence of heavy tissue clutter showed significantly improved computational performance using the proposed methods with minimal deterioration to the clutter filter performance (less than 3-dB reduction in blood to clutter ratio, less than 0.2-cm²/s² increase in flow mean squared error, less than 0.1-cm/s increase in the standard deviation of the vessel blood flow signal, and less than 0.3-cm/s increase in tissue clutter velocity for both full SVD and rSVD when the downsampling factor was less than 20× ). The maximum acceleration was about threefold from randomized spatial downsampling, and approximately another threefold from rSVD. An in vivo rabbit kidney perfusion study showed that rSVD provided comparable performance to full SVD in clutter rejection in vivo (maximum difference of blood to clutter ratio was less than 0.6 dB), and random downsampling provided artifact-free perfusion imaging results when combined with both full SVD and rSVD. The blood to clutter ratio was still above 10 dB with a downsampling factor of 60× . We also demonstrated real-time microvessel imaging feasibility (~40-ms processing time) by combining rSVD with random downsampling. The findings and methods presented in this paper may greatly facilitate the new area of ultrafast microvessel imaging research.