150 research outputs found

    Comparison of single distance phase retrieval algorithms by considering different object composition and the effect of statistical and structural noise

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    Phase retrieval is a technique for extracting quantitative phase information from X-ray propagation-based phase-contrast tomography (PPCT). In this paper, the performance of different single distance phase retrieval algorithms will be investigated. The algorithms are herein called phase-attenuation duality Born Algorithm (PAD-BA), phase-attenuation duality Rytov Algorithm (PAD-RA), phase-attenuation duality Modified Bronnikov Algorithm (PAD-MBA), phase-attenuation duality Paganin algorithm (PAD-PA) and phase-attenuation duality Wu Algorithm (PAD-WA), respectively. They are all based on phase-attenuation duality property and on weak absorption of the sample and they employ only a single distance PPCT data. In this paper, they are investigated via simulated noise-free PPCT data considering the fulfillment of PAD property and weakly absorbing conditions, and with experimental PPCT data of a mixture sample containing absorbing and weakly absorbing materials, and of a polymer sample considering different degrees of statistical and structural noise. The simulation shows all algorithms can quantitatively reconstruct the 3D refractive index of a quasi-homogeneous weakly absorbing object from noise-free PPCT data. When the weakly absorbing condition is violated, the PAD-RA and PAD-PA/WA obtain better result than PAD-BA and PAD-MBA that are shown in both simulation and mixture sample results. When considering the statistical noise, the contrast-to-noise ratio values decreases as the photon number is reduced. The structural noise study shows that the result is progressively corrupted by ring-like artifacts with the increase of structural noise (i.e. phantom thickness). The PAD-RA and PAD-PA/WA gain better density resolution than the PAD-BA and PAD-MBA in both statistical and structural noise study

    Optic Cup Segmentation Using Large Pixel Patch Based CNNs

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    Optic cup(OC) segmentation on color fundus image is essential for the calculation of cup-to-disk ratio and fundus morphological analysis, which are very important references in the diagnosis of glaucoma. In this paper we proposed an OC segmentation method using convolutional neural networks(CNNs) to learn from big size patch belong to each pixel. The segmentation result is achieved by classification of each pixel patch and postprocessing. With large pixel patch, the network could learn more global information around each pixel and make a better judgement during classification. We tested this method on public dataset Drishti-GS and achieved average F-Score of 93.73% and average overlapping error of 12.25%, which is better than state-of-the-art algorithms. This method could be used for fundus morphological analysis, and could also be employed to other medical image segmentation works which the boundary of the target area is fuzzy

    Phase retrieval in quantitative x-ray microtomography with a single sample-to-detector distance

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    Phase retrieval extracts quantitative phase information from x-ray propagation-based phase-contrast images. Notwithstanding inherent approximations, phase retrieval using a single sample-to-detector distance (SDD) is very attractive, because it imposes no setup complications or additional radiation dose compared to absorption-based imaging. Considering the phase-attenuation duality (Δ =ÎŽ/ÎČ, where Δ is constant), a simple absorption correction factor is proposed for the modified Bronnikov algorithm in x-ray propagation-based phase-contrast computed tomography (PPCT). Moreover, a practical method for calculating the optimal Δ value is proposed, which requires no prior knowledge of the sample. Tests performed on simulation and experimental data successfully distinguished different materials in a quasihomogeneous and weakly absorbing sample from a single SDD-PPCT data point

    Salvianolic Acid B Prevents Arsenic Trioxide-Induced Cardiotoxicity In Vivo and Enhances Its Anticancer Activity In Vitro

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    Clinical attempts to reduce the cardiotoxicity of arsenic trioxide (ATO) without compromising its anticancer activities remain to be an unresolved issue. In this study, we determined whether Sal B can protect against ATO-induced cardiac toxicity in vivo and increase the toxicity of ATO toward cancer cells. Combination treatment of Sal B and ATO was investigated using BALB/c mice and human hepatoma (HepG2) cells and human cervical cancer (HeLa) cells. The results showed that the combination treatment significantly improved the ATO-induced loss of cardiac function, attenuated damage of cardiomyocytic structure, and suppressed the ATO-induced release of cardiac enzymes into serum in BALB/c mouse models. The expression levels of Bcl-2 and p-Akt in the mice treated with ATO alone were reduced, whereas those in the mice given the combination treatment were similar to those in the control mice. Moreover, the combination treatment significantly enhanced the ATO-induced cytotoxicity and apoptosis of HepG2 cells and HeLa cells. Increases in apoptotic marker cleaved poly (ADP-ribose) polymerase and decreases in procaspase-3 expressions were observed through western blot. Taken together, these observations indicate that the combination treatment of Sal B and ATO is potentially applicable for treating cancer with reduced cardiotoxic side effects

    Dual-attention Focused Module for Weakly Supervised Object Localization

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    The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.Comment: 8 pages, 6 figures and 4 table

    A simplified edge illumination set-up for quantitative phase contrast mammography with synchrotron radiation at clinical doses

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    This work presents the first study of x-ray phase contrast imaging based on a simple implementation of the edge illumination method (EIXPCi) in the field of mammography with synchrotron radiation. A simplified EIXPCi set-up was utilized to study a possible application in mammography at clinical doses. Moreover, through a novel algorithm capable of separating and quantifying absorption and phase perturbations of images acquired in EIXPCi modality, it is possible to extract quantitative information on breast images, allowing an accurate tissue identification. The study was carried out at the SYRMEP beamline of Elettra synchrotron radiation facility (Trieste, Italy), where a mastectomy specimen was investigated with the EIXPCi technique. The sample was exposed at three different energies suitable for mammography with synchrotron radiation in order to test the validity of the novel algorithm in extracting values of linear attenuation coefficients integrated over the sample thickness. It is demonstrated that the quantitative data are in good agreement with the theoretical values of linear attenuation coefficients calculated on the hypothesis of the breast with a given composition. The results are promising and encourage the current efforts to apply the method in mammography with synchrotron radiation

    Circulating miRNAs act as potential biomarkers for asthma

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    BackgroundIdentification of new clinical markers contributes to a better understanding of the pathogenesis of asthma. Considering the crucial role of LIGHT in asthma, it may become a potential target for asthma. The aim of current study was to determine if circulating microRNAs (miRNAs) targeting LIGHT may be used as diagnostic biomarkers to distinguish asthma.MethodsBlood serum from a cohort of 60 subjects, including 20 cases with mild asthma, 20 cases with moderate-to-severe asthma, and 20 healthy controls were included. Serum was analyzed for circulating miRNAs profiles through miRNAs microarray. Real Time PCR was conducted to verify the results of miRNA microarray. Correlations between circulating miRNAs targeting LIGHT and clinical characteristics were investigated.ResultsA total of 365 miRNAs were differentially expressed in asthma patients. Among them, miR-107 and miR-140-5p were found to target LIGHT, and varied in asthmatics. Additionally, miR-107 and miR-140-5p expressions were positively correlated with the absolute value of peripheral eosinophils. Finally, miR-140-5p and miR-107 were demonstrated to have good diagnostic efficacy for asthma (AUC= 0.8667 and 0.9400) with good sensitivity (0.8000 and 0.8667,respectively) and specificity (0.8667 and 0.867). Thus, circulating miRNAs expressed differentially between healthy control and asthma patients.ConclusionPlasma miR-140-5p and miR-107 can be used as diagnostic biomarkers to distinguish patients with asthma from healthy control, and may take part in asthma pathogenesis by negatively regulating LIGHT. Further research was needed to evaluate their roles as potential biomarkers in the diagnosis of asthma
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