Competitive Swarm Optimized SVD Clutter Filtering for Ultrafast Power Doppler Imaging.

Ultrafast power Doppler imaging (uPDI) can significantly increase the sensitivity of resolving small vascular paths in ultrasound. While clutter filtering is a fundamental and essential method to realize uPDI, it commonly uses singular value decomposition (SVD) to suppress clutter signals and noise. However, current SVD-based clutter filters using two cutoffs cannot ensure sufficient separation of tissue, blood, and noise in uPDI. This article proposes a new competitive swarm-optimized SVD clutter filter to improve the quality of uPDI. Specifically, without using two cutoffs, such a new filter introduces competitive swarm optimization (CSO) to search for the counterparts of blood signals in each singular value. We validate the CSO-SVD clutter filter on public in vivo datasets. The experimental results demonstrate that our method can achieve higher contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and blood-to-clutter ratio (BCR) than the state-of-the-art SVD-based clutter filters, showing a better balance between suppressing clutter signals and preserving blood signals. Particularly, our CSO-SVD clutter filter improves CNR by 0.99 ± 0.08 dB, SNR by 0.79 ± 0.08 dB, and BCR by 1.95 ± 0.03 dB when comparing a spatial-similarity-based SVD clutter filter in the in vivo dataset of rat brain bolus.

Doppler and Pair-Wise Optical Flow Constrained 3D Motion Compensation for 3D Ultrasound Imaging.

Volumetric (3D) ultrasound imaging using a 2D matrix array probe is increasingly developed for various clinical procedures. However, 3D ultrasound imaging suffers from motion artifacts due to tissue motions and a relatively low frame rate. Current Doppler-based motion compensation (MoCo) methods only allow 1D compensation in the in-range dimension. In this work, we propose a new 3D-MoCo framework that combines 3D velocity field estimation and a two-step compensation strategy for 3D diverging wave compounding imaging. Specifically, our framework explores two constraints of a round-trip scan sequence of 3D diverging waves, i.e., Doppler and pair-wise optical flow, to formulate the estimation of the 3D velocity fields as a global optimization problem, which is further regularized by the divergence-free and first-order smoothness. The two-step compensation strategy is to first compensate for the 1D displacements in the in-range dimension and then the 2D displacements in the two mutually orthogonal cross-range dimensions. Systematical in-silico experiments were conducted to validate the effectiveness of our proposed 3D-MoCo method. The results demonstrate that our 3D-MoCo method achieves higher image contrast, higher structural similarity, and better speckle patterns than the corresponding 1D-MoCo method. Particularly, the 2D cross-range compensation is effective for fully recovering image quality.

Automatic Recognition of Ocular Surface Diseases on Smartphone Images Using Densely Connected Convolutional Networks.

Ocular surface disorder is one of common and prevalence eye diseases and complex to be recognized accurately. This work presents automatic classification of ocular surface disorders in accordance with densely connected convolutional networks and smartphone imaging. We use various smartphone cameras to collect clinical images that contain normal and abnormal, and modify end-to-end densely connected convolutional networks that use a hybrid unit to learn more diverse features, significantly reducing the network depth, the total number of parameters and the float calculation. The validation results demonstrate that our proposed method provides a promising and effective strategy to accurately screen ocular surface disorders. In particular, our deeply learned smartphone photographs based classification method achieved an average automatic recognition accuracy of 90.6%, while it is conveniently used by patients and integrated into smartphone applications for automatic patient-self screening ocular surface diseases without seeing a doctor in person in a hospital.

ApodNet: Learning for High Frame Rate Synthetic Transmit Aperture Ultrasound Imaging.

Two-way dynamic focusing in synthetic transmit aperture (STA) beamforming can benefit high-quality ultrasound imaging with higher lateral spatial resolution and contrast resolution. However, STA requires the complete dataset for beamforming in a relatively low frame rate and transmit power. This paper proposes a deep-learning architecture to achieve high frame rate STA imaging with two-way dynamic focusing. The network consists of an encoder and a joint decoder. The encoder trains a set of binary weights as the apodizations of the high-frame-rate plane wave transmissions. In this respect, we term our network ApodNet. The decoder can recover the complete dataset from the acquired channel data to achieve dynamic transmit focusing. We evaluate the proposed method by simulations at different levels of noise and in-vivo experiments on the human biceps brachii and common carotid artery. The experimental results demonstrate that ApodNet provides a promising strategy for high frame rate STA imaging, obtaining comparable lateral resolution and contrast resolution with four-times higher frame rate than conventional STA imaging in the in-vivo experiments. Particularly, ApodNet improves contrast resolution of the hypoechoic targets with much shorter computational time when compared with other high-frame-rate methods in both simulations and in-vivo experiments.

Compressed sensing reconstruction of synthetic transmit aperture dataset for volumetric diverging wave imaging.

A high volume rate and high performance ultrasound imaging method based on a matrix array is proposed by using compressed sensing (CS) to reconstruct the complete dataset of synthetic transmit aperture (STA) from three-dimensional (3D) diverging wave transmissions (i.e. 3D CS-STA). Hereto, a series of apodized 3D diverging waves are transmitted from a fixed virtual source, with the ith row of a Hadamard matrix taken as the apodization coefficients in the ith transmit event. Then CS is used to reconstruct the complete dataset, based on the linear relationship between the backscattered echoes and the complete dataset of 3D STA. Finally, standard STA beamforming is applied on the reconstructed complete dataset to obtain the volumetric image. Four layouts of element numbering for apodizations and transmit numbers of 16, 32 and 64 are investigated through computer simulations and phantom experiments. Furthermore, the proposed 3D CS-STA setups are compared with 3D single-line-transmit (SLT) and 3D diverging wave compounding (DWC). The results show that, (i) 3D CS-STA has competitive lateral resolutions to 3D STA, and their contrast ratios (CRs) and contrast-to-noise ratios (CNRs) approach to those of 3D STA as the number of transmit events increases in noise-free condition. (ii) the tested 3D CS-STA setups show good robustness in complete dataset reconstruction in the presence of different levels of noise. (iii) 3D CS-STA outperforms 3D SLT and 3D DWC. More specifically, the 3D CS-STA setup with 64 transmit events and the Random layout achieves ~31% improvement in lateral resolution, ~14% improvement in ratio of the estimated-to-true cystic areas, a higher volume rate, and competitive CR/CNR when compared with 3D DWC. The results demonstrate that 3D CS-STA has great potential of providing high quality volumetric image with a higher volume rate.

Doppler-Based Motion Compensation Strategies for 3-D Diverging Wave Compounding and Multiplane-Transmit Beamforming: A Simulation Study.

Fast imaging of the heart has shown promise toward bringing new diagnostic information. Although most studies to date have been based on 2-D imaging technology, the ultimate diagnostic tool would enable fast 3-D echocardiography. Hereto, 3-D diverging wave compounding (DWC) and 3-D multiline-transmit (MLT) beamforming have recently been proposed. Moreover, in our recent study, a hybrid technique was proposed in which multiple planar diverging waves were transmitted [i.e., multiplane-transmit (MPT)]. The proposed 3MPT sequence was demonstrated to outperform $9 \times 9$ DWC and 16MLT-4MLA (i.e., multiline acquisition) while imaging moving targets. However, none of the investigated beamforming techniques made use of motion compensation (MoCo) strategies. In this paper, we therefore propose Doppler-based MoCo strategies for 3-D DWC and MPT and test them via computer simulations. It is demonstrated that the MoCo strategies proposed for both DWC and MPT are effective and significantly restore image quality. Moreover, the MPT beamforming with MoCo outperforms $9 \times 9$ DWC with MoCo in terms of contrast ratio and contrast-to-noise ratio. The proposed MPT beamforming with MoCo thus provides volumetric images with relatively high temporal resolution (~66 Hz) and high image quality that is minimally affected by motion artifacts.

Feasibility of Multiplane-Transmit Beamforming for Real-Time Volumetric Cardiac Imaging: A Simulation Study.

Today's 3-D cardiac ultrasound imaging systems suffer from relatively low spatial and temporal resolution, limiting their applicability in daily clinical practice. To address this problem, 3-D diverging wave imaging with spatial coherent compounding (DWC) as well as 3-D multiline-transmit (MLT) imaging have recently been proposed. Currently, the former improves the temporal resolution significantly at the expense of image quality and the risk of introducing motion artifacts, whereas the latter only provides a moderate gain in volume rate but mostly preserves quality. In this paper, a new technique for real-time volumetric cardiac imaging is proposed by combining the strengths of both approaches. Hereto, multiple planar (i.e., 2-D) diverging waves are simultaneously transmitted in order to scan the 3-D volume, i.e., multiplane transmit (MPT) beamforming. The performance of a 3MPT imaging system was contrasted to that of a 3-D DWC system and that of a 3-D MLT system by computer simulations during both static and moving conditions of the target structures while operating at similar volume rate. It was demonstrated that for stationary targets, the 3MPT imaging system was competitive with both the 3-D DWC and 3-D MLT systems in terms of spatial resolution and sidelobe levels (i.e., image quality). However, for moving targets, the image quality quickly deteriorated for the 3-D DWC systems while it remained stable for the 3MPT system while operating at twice the volume rate of the 3-D-MLT system. The proposed MPT beamforming approach was thus demonstrated to be feasible and competitive to state-of-the-art methodologies.

Performance optimization of lateral displacement estimation with spatial angular compounding.

Elastography provides tissue mechanical information to differentiate normal and disease states. Nowadays, axial displacement and strain are usually estimated in clinical practice whereas lateral estimation is rarely used given that its accuracy is typically one order of magnitude worse than that of axial estimation. To improve the performance of lateral estimation, spatial angular compounding of multiple axial displacements along ultrasound beams transmitting in different steering angles was previously proposed. However, few studies have been conducted to evaluate the influence of key factors such as grating lobe noise (GLN), the number of steering angles (NSA) and maximum steering angle (MSA) in terms of performance optimization. The aim of this study was thus to investigate the effects of these factors through both computer simulations and phantom experiments. Only lateral rigid motion was considered in this study to separate its effects from those of axial and lateral strains on lateral displacement estimation. The performance as indicated by the root mean square error (RMSE) and standard deviation (SD) of the estimated lateral displacements validates the capability of spatial angular compounding in improving the performance of lateral estimation. It is necessary to filter the GLN for better estimation, and better performance is associated with a larger NSA and bigger MSA in both simulations and experiments, which is in agreement with the theoretical analysis. As indicated by the RMSE and SD, two steering angles with a larger steering angle are recommended. These results could provide insights into the performance optimization of lateral displacement estimation with spatial angular compounding.

A novel biosensor based on single-layer MoS2 nanosheets for detection of Ag(+).

In this work, we use for the first time single layer MoS2 as the fluorescence quencher to design a detection method for Ag(+) with excellent robustness, selectivity and sensitivity. To maintain the ultrathin MoS2, bulk MoS2 materials have been exfoliated by intercalation with lithium followed by reaction with water. As-prepared two-dimensional MoS2 not only has good water-solubility but also obtains high fluorescence quenching efficiency within 5 min. Importantly, the detection limit of this assay for Ag(+) (1 nM) was lower than the maximum limitation guided by the United States Environmental Protection Agency (EPA) and the World Health Organization (WHO). Further, this new Ag(+) probe was demonstrated in monitoring Ag(+) in lake water samples with satisfactory results.

Catalytic strategy for efficient degradation of nitroaromatic pesticides by using gold nanoflower.

In this contribution, we report a new type of Au nanoflower-based nitroaromatic pesticide degradation platform that is fast, efficient, and simple. We found a straightforward, economically viable, and "green" approach for the synthesis and stabilization of relatively monodisperse Au nanoflowers by using nontoxic chemical of hydroxylamine (NH2OH) without stabilizer and the adjustment of the pH environment. This experiment shows that these Au nanoflowers function as effective catalyst for the reduction of pendimethalin in the presence of NaBH4 (otherwise unfeasible if NaBH4 is the only agent employed), which was reflected by the UV/vis spectra of the catalytic reaction kinetics. Importantly, the novel degradation platform could be put in use in two different practical soil samples with satisfactory results under laboratory conditions. To demonstrate the feasibility and universality of our design, two other nitroaromatic pesticides, trifluralin, and p-nitrophenol, were selected and were successfully degraded using this degradation platform.

Detection of Ag⁺ ions and cysteine based on chelation actions between Ag⁺ ions and guanine bases.

A selective and sensitive fluorescence biosensor for Ag(+) ions and cysteine (Cys) was developed based on the chelation actions between Ag(+) ions and guanine bases of G-rich fluorogenic oligonucleotide (FAM-ssDNA) and the different electrostatic affinity between FAM-ssDNA and graphene oxide (GO). FAM-ssDNA adsorbed onto the surface of GO through π-π stacking interaction between the ring structure in the nucleobases and the hexagonal cells of GO, and the fluorescence of the dye was quenched. In the presence of Ag(+) ions, the random coil structure changed into a G-Ag(+) architecture. As a result, the binding released FAM-ssDNA signal probe from the surface of GO, which disrupted the energy transfer from FAM-ssDNA to GO, recovering the fluorescence emission of FAM-ssDNA. On the other hand, because Cys was a strong Ag(+) ions binder, it could deactivate the sensor fluorescence by rewrapping FAM-ssDNA around GO. In this way, these changes in fluorescence intensity allowed the selective detection of Ag(+) ions and Cys.