During the last decade, various methods for 2D array design have been developed for real-time 3D ultrasonic imaging. Most of the methods concentrated on how to reduce the number of elements and channels to overcome the difficulties in array fabrication and mass data processing. Few works focused on the 2D array beamforming techniques to narrow the main lobe width and suppress the side and grating lobe levels, thus improve the 3D image quality. Coherence imaging (CI) has been verified to suppress the side and grating lobes of the 2D ultrasound images in an effective way. It was based on a statistical analysis of the received signal dispersion. In this paper, two kinds of CI, coherence factor (CF) and sign coherence factor (SCF) are modified for 2D arrays and combined with array designs to improve the 3D ultrasound image qualities. The simulation results of point spread functions show that the main lobe width is narrowed from 1.26mm to 1.01mm and the side lobe level is suppressed from -48.79dB to -79.31dB for dense arrays with CF. Similar simulation results can be obtained for other array designs. The combination of CI and 2D array design provides a potential approach to increase the 3D imaging resolution and contrast without increasing the system complexity.
To reconstruct ultrasonic B-mode image with improved quality, it is desirable to eliminate the contributions originating from the off-axis reflectors in the backscattered echoes. A new coherence factor based on phasor dispersion (PDCF) is presented for side lobe and clutter suppression. This method utilizes the coherence of complex-value aperture data to determine the interference degree to the main target (or, the amount of main lobe versus clutter) in the received signals. Based on a measurement of phasor dispersion and a theoretical maximum estimated by the statistics of echo envelope, PDCF is devised and weights the coherent beamsum. It results in images with reduced clutter and enhanced contrast. The predicted improvements on image quality were verified by simulated point and cyst phantoms using Field II.
Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with no cure except transplantation. Abnormal alveolar epithelial regeneration is a key driver of IPF development. The function of Yes1 Associated Transcriptional Regulator (YAP) in alveolar regeneration and IPF pathogenesis remains elusive. Here, we first revealed the activation of YAP in alveolar epithelium 2 cells (AEC2s) from human IPF lungs and fibrotic mouse lungs. Notably, conditional deletion of YAP in mouse AEC2s exacerbated bleomycin-induced pulmonary fibrosis. Intriguingly, we showed in both conditional knockout mice and alveolar organoids that YAP deficiency impaired AEC2 proliferation and differentiation into alveolar epithelium 1 cells (AEC1s). Mechanistically, YAP regulated expression levels of genes associated with cell cycle progression and AEC1 differentiation. Furthermore, overexpression of YAP in vitro promoted cell proliferation. These results indicate the critical role of YAP in alveolar regeneration and IPF pathogenesis. Our findings provide new insights into the regulation of alveolar regeneration and IPF pathogenesis, paving the road for developing novel treatment strategies.
The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM model. The results indicate that combining the semi-transmission hyperspectral imaging technology with CARS-SPA and AFSA-SVM can accurately detect hollow heart of potato, and also provide technical support for rapid non-destructive detecting of hollow heart of potato.
In recent years, many research studies have been carried out on ultrasound computed tomography (USCT) for its application prospect in early diagnosis of breast cancer. This paper applies four kinds of coherence-factor-like beamforming methods to improve the image quality of synthetic aperture focusing method for USCT, including the coherence-factor (CF), the phase coherence factor (PCF), the sign coherence factor (SCF) and the spatial smoothing coherence factor (SSCF) (proposed in our previous work). The performance of these methods was tested with simulated raw data which were generated by the ultrasound simulation software PZFlex 2014. The simulated phantom was set to be water of 4cm diameter with three nylon objects of different diameters inside. The ring-type transducer had 72 elements with a center frequency of 1MHz. The results show that all the methods can reveal the biggest nylon circle with the radius of 2.5mm. SSCF gets the highest SNR among the proposed methods and provides a more homogenous background. None of these methods can reveal the two smaller nylon circles with the radius of 0.75mm and 0.25mm. This may be due to the small number of elements.
The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.
Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression.
Chronic bone loss is an under-recognized complication of malaria, the underlying mechanism of which remains incompletely understood. We have previously shown that persistent accumulation of Plasmodium products in the bone marrow leads to chronic inflammation in osteoblast (OB) and osteoclast (OC) precursors causing bone loss through MyD88, an adaptor molecule for diverse inflammatory signals. However, the specific contribution of MyD88 signaling in OB or OC precursors in malaria-induced bone loss remains elusive. To assess the direct cell-intrinsic role of MyD88 signaling in adult bone metabolism under physiological and infection conditions, we used the Lox-Cre system to specifically deplete MyD88 in the OB or OC lineages. Mice lacking MyD88 primarily in the maturing OBs showed a comparable decrease in trabecular bone density by microcomputed tomography to that of controls after Plasmodium yoelii non-lethal infection. In contrast, mice lacking MyD88 in OC precursors showed significantly less trabecular bone loss than controls, suggesting that malaria-mediated inflammatory mediators are primarily controlled by MyD88 in the OC lineage. Surprisingly, however, depletion of MyD88 in OB, but not in OC, precursors resulted in reduced bone mass with decreased bone formation rates in the trabecular areas of femurs under physiological conditions. Notably, insulin-like growth factor-1, a key molecule for OB differentiation, was significantly lower locally and systemically when MyD88 was depleted in OBs. Thus, our data demonstrate an indispensable intrinsic role for MyD88 signaling in OB differentiation and bone formation, while MyD88 signaling in OC lineages plays a partial role in controlling malaria-induced inflammatory mediators and following bone pathology. These findings may lead to the identification of novel targets for specific intervention of bone pathologies, particularly in malaria-endemic regions.
Delay-and-sum (DAS) beamformer is extensively used in ultrasound imaging. However, the DAS beamformed signals have wide main lobe widths and high side lobe levels, which result in images with limited resolution and low contrast. Recently, a new signal processing method named phase coherence imaging (PCI) for side and grating lobes suppression was proposed. It was based on a statistical analysis of the phase dispersion in the received signals. The contrast could be significantly enhanced. For spatial resolution improvement, adaptive minimum variance (MV)-based beamformer presented in the ultrasound imaging literatures shows great potentials by minimizing off-axis signals, while keeping on-axis ones. In this paper, MV beamforming combined with PCI is introduced to effectively increase the imaging resolution and contrast simultaneously and outperform both MV and PCI beamformers. Two phase coherence factors, the phase coherence factor (PCF) and the sign coherence factor (SCF), are computed based on the measurement of the phase diversity of the received aperture data, and then used to weight the MV beamformed channel sum output. Simulations with point and cyst phantoms using FIELD II demonstrate the expected performance of the proposed beamforming method.
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