Row-column arrays have been shown to be able to generate 3-D ultrafast ultrasound images with an order of magnitude less independent electronic channels than traditional 2-D matrix arrays. ...Unfortunately, row-column array images suffer from major imaging artefacts due to high side-lobes, particularly when operating at high frame rates. This paper proposes a row-column specific beamforming technique, for orthogonal plane wave transmissions, that exploits the incoherent nature of certain row-column array artefacts. A series of volumetric images are produced using row or column transmissions of 3-D plane waves. The voxel-wise geometric mean of the beamformed volumetric images from each row and column pair is taken prior to compounding, which drastically reduces the incoherent imaging artefacts in the resulting image compared to traditional coherent compounding. The effectiveness of this technique was demonstrated in silico and in vitro, and the results show a significant reduction in side-lobe level with over 16 dB improvement in side-lobe to main-lobe energy ratio. Significantly improved contrast was demonstrated with contrast ratio increased by ~10dB and generalised contrast-to-noise ratio increased by 158% when using the proposed new method compared to existing delay and sum during in vitro studies. The new technique allowed for higher quality 3-D imaging whilst maintaining high frame rate potential.
Acoustic super-resolution imaging has allowed the visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of ...many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7-cm depth and 2.3-MHz center frequency. We corroborate these findings with simulation results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental crosssectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 times reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection, and 2-D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as 900 μm at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is, therefore, expected to considerably improve the accuracy of super-resolution.
In this study, a technique for high-frame-rate ultrasound imaging velocimetry (UIV) is extended first to provide more robust quantitative flow velocity mapping using ensemble correlation of images ...without coherent compounding, and second to generate spatio-temporal wall shear stress (WSS) distribution. A simulation model, which couples the ultrasound simulator with analytical flow solution, was implemented to evaluate its accuracy. It is shown that the proposed approach can reduce errors in velocity estimation by up to 10-fold in comparison with the coherent correlation approach. Mean errors (ME) of 3.2% and 8.6% were estimated under a steady flow condition, while 3.0% and 10.6% were found under a pulsatile condition for the velocity and wall shear rate (WSR) measurement, respectively. Appropriate filter parameters were selected to constrain the velocity profiles before WSR estimations and the effects of incorrect wall tracking were quantified under a controlled environment. Although accurate wall tracking is found to be critical in WSR measurement (as a 200 µm deviation from the wall may yield up to a 60% error), this can be mitigated by HFR imaging (of up to 10 kHz) with contrast agents, which allow for improved differentiation of the wall-fluid boundaries. In vitro investigations on two carotid bifurcation phantoms, normal and diseased, were conducted, and their relative differences in terms of the flow patterns and WSR distribution were demonstrated. It is shown that high-frame-rate UIV technique can be a non-invasive tool to measure quantitatively the spatio-temporal velocity and WSS distribution.
The very high frame rate afforded by ultrafast ultrasound, combined with microbubble contrast agents, opens new opportunities for imaging tissue microvasculature. However, new imaging paradigms are ...required to obtain superior image quality from the large amount of acquired data while allowing real-time implementation. In this paper, we report a technique-acoustic subaperture processing (ASAP)-capable of generating very high contrast/signal-to-noise ratio (SNR) images of macroand microvessels, with similar computational complexity to classical power Doppler (PD) imaging. In ASAP, the received data are split into subgroups. The reconstructed data from each subgroup are temporally correlated over frames to generate the final image. As signals in subgroups are correlated but the noise is not, this substantially reduces the noise floor compared to PD. Using a clinical imaging probe, the method is shown to visualize vessels down to 200 μm with a SNR of 10 dB higher than PD and to resolve microvascular flow/perfusion information in rabbit kidneys noninvasively in vivo at multiple centimeter depths. With careful filter design, the technique also allows the estimation of flow direction and the separation of fast flow from tissue perfusion. ASAP can readily be implemented into hardware/firmware for real-time imaging and can be applied to contrast enhanced and potentially noncontrast imaging and 3-D imaging.
This paper investigates medical providers’ supply curve under universal healthcare system with global budgeting, which theory predicts to be either positive or negative sloping. Using the population ...data of medical providers’ services and exogenous shifted budgets in Taiwan, empirical evidence shows that the dentists and Chinese herb practitioners maintained positive sloping supply curves. Hospitals and clinics that practice Western medicine were found to have negative sloping supply curves. The latter results indicate medical providers have incentives to provide excessive services under global budgeting, even when this drives down the price of their services provided.
Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE ...perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2-D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods, including the classic RF and its variants, active shape model and image registration.
•A deep neural network approach is proposed for predicting carbon dioxide plumes.•Plume predictions can be generated given permeability maps and injection conditions.•A transfer learning technique is ...proposed to improve the extrapolation accuracy.
This paper demonstrates a deep neural network approach for predicting carbon dioxide (CO2) plume migration from an injection well in heterogeneous formations with high computational efficiency. With the data generation and training procedures proposed in this paper, we show that the deep neural network model can generate predictions of CO2 plume migration that are as accurate as traditional numerical simulation, given input variables of a permeability field, an injection duration, injection rate, and injection location. The neural network model can deal with permeability fields that have high degrees of heterogeneity. Unlike previous studies which did not consider the effect of buoyancy, here we also show that the neural network model can learn the consequences of the interplay of gravity, viscous, and capillary forces, which is critically important for predicting CO2 plume migration. The neural network model has an excellent ability to generalize within the training data ranges and to a limited extent, the ability to extrapolate beyond the training data ranges. To improve the prediction accuracy when the neural network model needs to extrapolate to situations or parameters not contained in the training set, we propose a transfer learning (fine-tuning) procedure that can quickly teach the trained neural network model new information without going through massive data collection and retraining. With the approaches described in this paper, we have demonstrated many of the building blocks required for developing a general-purpose neural network for predicting CO2 plume migration away from an injection well.
Layer-by-layer self-assembly of polyelectrolytes is one of the most promising methods for preparing nanofiltration membranes. Here poly (sodium 4-styrenesulfonate) (PSS) and poly ...(diallyldim-ethylammonium chloride) (PDADMAC) were chosen to prepare polyelectrolyte multilayer (PEM) membrane. The effects of background salt concentration, PSS concentration, background salt types, the number of layers and the outermost functional groups on the separation performance of the PEM were investigated systematically. Molecular dynamic simulation was employed to simulate the influence of background salt on the diffusion of PDADMAC. The simulation results agree well with the experimental results. The optimized (PSS/PDADMAC)4.5 membrane with Na2SO4 as background salt shows permeance of 15.9 LMH/bar, Na2SO4 rejection of 98.2 %, and an excellent stability. Our study provides both theoretical and experimental supports for the preparation of PEM membranes using LBL method.
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•Affecting factors were systematically investigated for polyelectrolyte membrane prepared by layer-by-layer self-assembly.•MD simulation results show that different ions influence the diffusion rate of polyelectrolyte.•The obtained membrane shows excellent separation performance for corresponding salts.
The structure of microvasculature cannot be resolved using conventional ultrasound (US) imaging due to the fundamental diffraction limit at clinical US frequencies. It is possible to overcome this ...resolution limitation by localizing individual microbubbles through multiple frames and forming a superresolved image, which usually requires seconds to minutes of acquisition. Over this time interval, motion is inevitable and tissue movement is typically a combination of large- and small-scale tissue translation and deformation. Therefore, super-resolution (SR) imaging is prone to motion artifacts as other imaging modalities based on multiple acquisitions are. This paper investigates the feasibility of a two-stage motion estimation method, which is a combination of affine and nonrigid estimation, for SR US imaging. First, the motion correction accuracy of the proposed method is evaluated using simulations with increasing complexity of motion. A mean absolute error of 12.2 \mu \text{m} was achieved in simulations for the worst-case scenario. The motion correction algorithm was then applied to a clinical data set to demonstrate its potential to enable in vivo SR US imaging in the presence of patient motion. The size of the identified microvessels from the clinical SR images was measured to assess the feasibility of the two-stage motion correction method, which reduced the width of the motion-blurred microvessels to approximately 1.5-fold.
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