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Surface plasmon resonance (SPR) is used to infer information about a sample that is in contact with an Au-coated glass slide coupled to the SPR prism. Shifts in the angle of the "SPR minimum reflection" can be related to changes in the refractive index (and/or thickness) of the sample that is in contact with the Au film, which can then be used to determine the concentration of an analyte in that sample. Here, we show that by depositing a layer of poly(N-isopropylacrylamide-co-acrylic acid) [p(NIPAm-co-AAc)] microgel on the SPR's Au film, with a subsequent layer of Au deposited on top of the microgels, the sensitivity of SPR to changes in solution properties can be enhanced. We investigated the sensitivity of the SPR to changes in the temperature of water in contact with the SPR's Au film as a function of the microgel immobilization density and the thickness of the Au layer deposited on the microgel layer. The data revealed that the SPR's Au film densely coated with microgels, with 5 nm of Au deposited, exhibited the maximal enhancement. The plasmon coupling effect between the additional Au film on the microgels and the SPR's Au film was further confirmed by 3D finite difference time domain simulations.

Photon-counting spectral computed tomography (CT) is capable of material characterization and can improve diagnostic performance over traditional clinical CT. However, it suffers from photon count starving for each individual energy channel which may cause severe artifacts in the reconstructed images. Furthermore, since the images in different energy channels describe the same object, there are high correlations among different channels. To make full use of the inter-channel correlations and minimize the count starving effect while maintaining clinically meaningful texture information, this paper combines a region-specific texture model with a low-rank correlation descriptor as an a priori regularization to explore a superior texture preserving Bayesian reconstruction of spectral CT. Specifically, the inter-channel correlations are characterized by the low-rank representation, and the inner-channel regional textures are modeled by a texture preserving Markov random field. In other words, this paper integrates the spectral and spatial information into a unified Bayesian reconstruction framework. The widely-used Split-Bregman algorithm is employed to minimize the objective function because of the non-differentiable property of the low-rank representation. To evaluate the tissue texture preserving performance of the proposed method for each channel, three references are built for comparison: one is the traditional CT image from energy integration detection. The second one is spectral images from dual-energy CT. The third one is individual channels images from custom-made photon-counting spectral CT. As expected, the proposed method produced promising results in terms of not only preserving texture features but also suppressing image noise in each channel, comparing to existing methods of total variation (TV), low-rank TV and tensor dictionary learning, by both visual inspection and quantitative indexes of root mean square error, peak signal to noise ratio, structural similarity and feature similarity.

Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g. human faces) from multiple slice (or color) images, a task similar to the polyp differentiation, given a large learning database. This study explores the potential of CNN learning from multiple slice (or feature) images to differentiate malignant from benign polyps from a relatively small database with pathological ground truth, including 32 malignant and 31 benign polyps represented by volumetric computed tomographic (CT) images. The feature image in this investigation is the gray-level co-occurrence matrix (GLCM). For each volumetric polyp, there are 13 GLCMs, computed from each of the 13 directions through the polyp volume. For comparison purpose, the CNN learning is also applied to the multi-slice CT images of the volumetric polyps. The comparison study is further extended to include Random Forest (RF) classification of the Haralick texture features (derived from the GLCMs). From the relatively small database, this study achieved scores of 0.91/0.93 (two-fold/leave-one-out evaluations) AUC (area under curve of the receiver operating characteristics) by using the CNN on the GLCMs, while the RF reached 0.84/0.86 AUC on the Haralick features and the CNN rendered 0.79/0.80 AUC on the multiple-slice CT images. The presented CNN learning from the GLCMs can relieve the challenge associated with relatively small database, improve the classification performance over the CNN on the raw CT images and the RF on the Haralick features, and have the potential to perform the clinical task of differentiating malignant from benign polyps with pathological ground truth.

Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

Computer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

Femtosecond laser pump-probe method is a useful tool to study the transient reflection phenomenon on metal films. Using double pump beams in femtosecond laser pump-probe system can both increase the electron non-equilibrium heat transport time and degrade the noise by adjust the experimental optical path. In this paper, the transient non-equilibrium heat transport processes of the copper-silver alloy films were studied by using femtosecond laser pump-probe method of double pump beams. The electron-lattice interactions were analysed and the electron non-equilibrium heat transport time was measured. The transient reflection phenomenons on copper-silver alloy films with the diverse film thickness were different, and the non-equilibrium heat transport time was inversely proportional to the film thickness. Two sudden changes which ascend abruptly appeared in the transient reflection signals, that is, twice electron non-equilibrium heat transport occurred in the copper-silver alloy films. With this method, the non-equilibrium heat transport time can be extend more than two times, which is expected to reduce the influence of thermal barrier and improve production efficiency.

Screening is an effective way to detect lung cancer early and can improve the survival rate significantly. The low-dose computed tomography (LdCT) is demanding for lung screening to ensure the exam radiation as low as reasonably possible. The statistical image reconstruction has shown great advantages in LdCT imaging, where many types of priors can be used as constrain for optimal images. The tissue-specific Markov random field (MRF) type texture prior (MRFt) was proposed in our previous work to address the clinical related texture information. For the chest scans, four tissue texture were extracted from regions of lung, bone, fat and muscle respectively. In this work, we focus on the region of interest, i.e. lung for the lung cancer screening. The quantitative texture analysis of normal and abnormal lung tissue was performed to address the following issues of the proposed MRFt model: (1) a more comprehensive understanding of the lung tissue texture (2) what MRF prior we should use for the abnormal lung tissue. Experiments results showed that normal lung tissue has texture similarity among different subjects. The robust similarity among humans laid the feasibility of building the lung tissue database for the LdCT imaging which has no previous FdCT scans. Different abnormal lung tissue varies significantly. There is no way to get the prior knowledge of lung nodules until the CT exam was performed.

Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.

Computed tomography (CT) of COVID-19 manifests a relatively global effect through the whole lungs, like peripheral ground glass, consolidation, reticular pattern, nodules etc. This characteristic effect renders the difficulties in differentiating COVID-19 from the normal body or other lung diseases by CT. This work presents a novel method to relieve the difficulties by reducing the global effect through the 3D whole lung volume into 2D-like domain. The hypothesis is that the lung tissue shares the similar anatomic structure within a small lung sub-volume for normal subjects. Therefore, the anatomic land-markers along the z-axis, denoted as Lung Marks are used to eliminate axial variable. Our experiments indicated that 30 Lung Marks are sufficient to eliminate the axial variable. The method computes texture measures from each 2D-like volumetric data and maps the measures on to the corresponding Lung Mark, resulting in a profile along the z-axis. The difference of the profiles between two different abnormalities is the proposed sensitive merit to differentiate COVID-19 cases from others in CT images. 48 COVID-19 cases and 48 normal screening cases were used to test the effectiveness of the proposed sensitive merit. Intensity and gradient based texture descriptors were computed from each axial cross image at the corresponding Lung Mark along the z-axis. Euclidean, Jaccard and Dice distances are calculated to generate the profiles of the proposed sensitive merit. Consistent results are observed across texture descriptor types and distance types in the texture measure between the normal and COVID-19 subjects. Uneven Profiles demonstrate the variation along the z-axis. With Lung Mark, the variation of texture descriptor has been reduced prominently. The Gradient based descriptor is more sensitive. Individual Haralick features analysis shows the 2nd and 10th dimensions are most distinguishable.

Dual energy computed tomography (DECT) expands applications of CT imaging in its capability to acquire two datasets, one at high and the other at low energies, and produces decomposed material images. Instead of being viewed simultaneously, the produced two series images are most often fused into a single optimum contrast image for disease diagnosis using a blending technique with a ratio of the two datasets. However, current existing methods apply blending technique on the individual reconstructed CT images, which suffer from beam hardening artifacts. Furthermore, how to select a reasonable ratio to improve the contrast resolution remains a practical problem in current DECT application. In order to alleviate the above two issues, we proposed an adaptive optimum contrast image acquisition strategy and demonstrated its success in improving our previous spectrum estimation-guided DECT (SEG-DECT) reconstruction. Specifically, the proposed strategy takes x-ray spectrum information into consideration to reconstruct the basis material density images by the SEG-DECT method to eliminate the effect of beam hardening. Furthermore, based on the statistical properties of the decomposed material images, an adaptive nonlinear blending technique is incorporated in the reconstruction of an optimum contrast CT image. Numerical experiments with the XCAT phantom containing a lesion showed that the proposed method significantly improves both the contrast-to-noise ratio and quality of fused CT images.

Data preparation for any machine learning process is of utmost importance to produce consistent and robust results. The challenges faced with identifying the colon polyps in computed tomographic colonography (CTC) images are that some polyps do not have a definite outline, some are coated by oral tag material due to poor preparation, and the intra-variation when multiple contributors work on the same dataset. This study aims to relieve the challenge by an iterative user-driven procedure, which starts by an expert to draw the initial borders of the colon polyps (or VOIs), followed by using computer aided classification (CAC) on an adequately grouped VOIs to find outliers. Then the expert examines the outliers for refining the VOIs and the CAC is repeated on the refined VOIs. This iterative procedure is repeated until a threshold is satisfied. The expert-driven CAC procedure was validated by experiments using three datasets. One small dataset containing 87 large polyp masses, and two large datasets containing 726 and 563 polyp masses varying in size from medium and small. Of the dataset with 87 polyps, 63 VOIs were constructed previously by three experts as the baseline, including 31 benign and 32 malignant. The remaining 24 (12 benign and 12 malignant) were added after going through the expert-driven CAC procedure (i.e. only one expert). The two large datasets had multiple contributors and each dataset could be split into several subgroups and cross validated using the highest performing subgroup as the baseline. The cross validation was performed using the grey-level co-occurrence measures of the VOIs, two-fold validation, and random forest classifier. The AUC score on the large polyp dataset remained the same as that of the baseline when the 24 new VOIs were added using the expert-driven CAC procedure, while varied by 4% if the procedure was not used. The AUC score on the medium and small polyp datasets had nominal increases up to 2% after expert-driven CAC procedure. Upon further examination on the-up-to 2% variation, the causes include flat small polyps and small polyps being submerged and/or surrounded by oral tagging materials. These causes of up to 2% variation are CTC data specific and acceptable. In conclusion, expert-driven CAC is important for large database construction. Key words: ML, Database, Colon polyps, CTC, CAC.

PURPOSE: Tissue texture has been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering X-ray exposure from full- toward low-/ultralow-dose level. Therefore, this paper aims to explore the texture-dose relationship by one tissue-specific pre-log Bayesian CT reconstruction algorithm. METHODS: To enhance the texture in ultralow-dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm, where shifted Poisson model is adapted to describe the statistical properties of pre-log data and a tissue-specific Markov random field (MRF) prior is used to incorporate tissue texture from previous full dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of dose levels from full dose (100mAs/120kVp) to ultralow dose (1mAs/120kVp) by introducing a quantitative texture-based evaluation metrics. RESULTS: Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7×7 Huber MRF weights (SP-Huber7). The SP-MRFt is a reasonably good tool to investigate the texture-dose relationship approaching the ultra-low dose end. The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. CONCLUSIONS: This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.

Machine learning, especially convolutional neural network (CNN) approach has been successfully applied in noise suppression in natural image. However, shifting from natural image to medical image filed remains challenging due to specific difficulties such as training samples limitation, clinically meaningful image quality requirement and so on. To address this challenge, one possible solution is to incorporate our human prior knowledge into the machine learning model to better benefit its power. Therefore, in this work, we propose one prior knowledge driven machine learning based approach for positron emission tomography (PET) sinogram data denoising. Two main properties of PET sinogram data were considered in CNN architecture design, which are the Poisson statistics of the data and different correlation strength in the detector and view directions. Specially, for the statistical property, the sparse non-local method was used. For the correlation property, separate convolution was applied in two directions respectively. Experimental results showed the proposed model outperform the CNN model without prior knowledge. Results also demonstrate our insight of applying human knowledge strength the power of machine learning in medical imaging field.

Photon-counting spectral computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among all channels. In addition, reconstruction of each energychannel image suffers photon count starving problem. To make full use of the correlation among different channels to suppress the data noise and enhance the tissue texture in reconstructing each energy-channel image, this paper proposed a tensor convolutional neural network (TCNN) architecture to learn a tissue-specific texture prior for PCCT reconstruction. Specifically, we first model the spatial texture prior information in each individual channel using a convolution neural network, and then extract the correlation information among different energy channels by merging the multi-channel networks. Finally, we integrate the TCNN as a prior into Bayesian reconstruction framework. To evaluate the tissue texture preserving performance of the proposed method for each channel, a vivid clinical phantom which can simulate the real tissue textures was employed. The improvement associated with TCNN is remarkable relative to simultaneous algebraic reconstruction technique (SART) and tensor dictionary learning (TDL) based reconstruction. The proposed method produced promising results in terms of not only preserving texture feature but also suppressing image noise in each channel. The proposed method outperforms the competing methods in both visual inspection and quantitative indexes of root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).

Deep learning such as Convolutional Neural Network (CNN) has demonstrated its superior in the field of image analysis. However, in the medical imaging field, deep learning faces more challenges for tumor classification in computer-aided diagnosis due to uncertainties of lesions including their size, scaling factor, rotation, shapes, etc. Thus, instead of feeding raw images, texture-based CNN model has been designed to classify the objects with their good attributes. For example, gray level co-occurrence matrix (GLCM) can be chosen as the descriptor of the texture pattern for many good properties such as uniform size, shape invariance, scaling invariance. However, there are many different texture metrics to measure the different texture patterns. Thus, an effective and efficient integration model is essential to further improve the classification performance from different texture patterns. In this paper, we proposed a multi-channel texture-based CNN model to effectively integrate intensity, gradient and curvature texture patterns together for differentiating the malignant from benign polyps. Performance was evaluated by the merit of area under the curve of receiver operating characteristics (AUC). Around 0.3~4.8% improvement has been observed by combining different texture patterns together. Finally, classification performance of AUC=86.7% has been achieved for a polyp mass dataset of 87 samples, which obtains 1.8% improvement compared with a state-of-the-art method. The results indicate that texture information from different metrics could be fused and classified with a better classification performance. It also sheds lights that data integration is important and indispensable to pursuit improvement in classification task.

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Pseudoginsenoside-F11 (PF11), an ocotillol-type ginsenoside, has been reported to exert neuroprotective effects on ischemic stroke induced by permanent and transient middle cerebral artery occlusion in experimental animals. The aim of the present study was to investigate the effect of PF11 on thromboembolic stroke in rats and its possible mechanisms on thromboinflammation. PF11 (4, 12, 36 mg/kg) was injected intravenously (i.v.) once a day for 3 consecutive days to male Wistar rats followed by embolic middle cerebral artery occlusion (eMCAO). The results showed that PF11 significantly reduced the cerebral infarction volume, brain edema and neurological deficits induced by eMCAO. Meanwhile, the thromboinflammation in the ischemic hemisphere was observed at 24 h after eMCAO, as indicated by the increased number of microvascular thrombus and inflammatory response. Moreover, eMCAO resulted in the up-regulation of platelet glycoprotein Ibα (GPIbα) and VI (GPVI), as well as the activation of contact-kinin pathway. Notably, PF11 significantly reversed all these changes. Furthermore, PF11 prevented the eMCAO-induced loss of tight junction proteins and up-regulation of matrix metalloproteinase-9 (MMP-9), thus leading to the alleviation of blood-brain barrier (BBB) damage. In conclusion, the present study revealed that thromboinflammation was induced in the ischemic hemisphere of rats after eMCAO and PF11 exerted marked protective effects against thromboembolic stroke by attenuating thromboinflammation and preventing BBB damage. This research further identifies the potential therapeutic role of PF11 for ischemic stroke.

The enhanced cooling performance and the reduced operational cost of the integrated radiative sky coolers are essentially necessitated in a wide range of applications, such as cooling of buildings, electronic devices, solar cells, power plants and personal wears, etc. The radiative sky coolers are enabled by radiating heat into deep space and reflecting sunlight without external energy input, thereby providing passive cooling. Previously, radiative sky cooling was limited to nocturnal applications; however, recent advancements in nanotechnology and metamaterials have altogether led to achieve sub-ambient temperatures under direct solar irradiation, creating diurnal applications. In addition to elaborate radiative heat transfer mechanism and evaluate the corresponding limitations, this review puts forth an in-depth study on the structural characteristics of broadband and selective band radiative sky coolers, the latest advancements in radiative materials and emerging applications of the radiative sky coolers. Most importantly, valuable insights are discussed regarding the barriers and scalability of radiative sky coolers on the commercial scale. It is therefore anticipated that a broader picture of the recent progress about radiative sky cooling as presented herein provides a great potential to boost up energy-saving, resolve water-scarcity, maximize power generation, minimize urban heat effects and so forth.

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies. Therefore, this paper aims to address two related issues for clinical usage of spectral CT, especially the photon counting CT (PCCT): (1) texture enhancement by spectral CT image reconstruction, and (2) spectral energy enriched tissue texture for improved lesion classification. For issue (1), we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory. Reconstruction results showed the proposed method outperforms existing methods of total variation (TV), low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise. For issue (2), this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs: one is the spectral images, another is the co-occurrence matrices (CMs) extracted from the spectral images, and the third one is the Haralick features (HF) extracted from the CMs. Studies were performed on simulated photon counting data by introducing attenuation-energy response curve to the traditional CT images from energy integration detectors. Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve (AUC) score by 7.3%, 0.42% and 3.0% for the spectral images, CMs and HFs respectively on the five-energy spectral data over the original single energy data only. The CM- and HF-inputs can achieve the best AUC of 0.934 and 0.927. This texture themed study shows the insight that incorporating clinical important prior information, e.g., tissue texture in this paper, into the medical imaging, such as the upstream image reconstruction, the downstream diagnosis, and so on, can benefit the clinical tasks.

Recently, deep learning such as Convolutional Neural Network (CNN) has shown its superior for the task of image classification. However, it faces great challenges in the medical imaging field, especially for tumor classification in computer-aided diagnosis, because there are many uncertainties of lesions including their size, scaling factor, rotation, shapes, etc. Hence, texture pattern is an option to be fed into the CNN model and among the texture patterns, gray level co-occurrence matrix (GLCM) can be chosen as the texture pattern for its several good properties such as uniform size, shape invariance, posture robustness, scaling invariance. For a 3D volume sample, instead of studying on the raw images, 13 independent GLCMs could be extracted based on the 13 digital distinct directions, and different sampling displacements have been recorded accordingly. Therefore, considering this multi-scale sampling is hypothesized to be optimal compared to without considering the multi-scale nature, we proposed an adaptive boosting learning (ABL) to examine the proposed multi-scale hypothesis. Following the multi-scale nature, the 13 directions can be grouped as three by different sampling displacements, and the ABL is applied to learn the best classification performance from the 3 subgroups. By comparing our ABL with and without the multi-scale nature, our experiments obtained a gain of 0.5~3.7% in terms of AUC (area under the ROC curve) for polyp classification, eventually reaching AUC of 91.13% for a small database of 63 samples with pathological reports. The gains reveal that the multi-scale GLCM-CNN model with ABL strategy can reduce the challenges for classification of limited number of tumors in medical imaging field.

Spectral CT works with multiple energy X-ray sources or energy recognized detector, which can obtain the attenuation map under different energy. Therefore, spectral CT can enhance the contrast between soft tissues and enrich the textures of colon polyps. In this paper, inspired by spectral CT, we proposed a novel data engineering method, which could prominently enhance colon polyp classification by the enriched tissue textures. 63 polyp volumetric CT images (31 benign and 32 malignant) were obtained from a clinical CT scanner with effective 75keV X-ray energy. All polyps were resected with pathology reports serving as ground truth. A linear scaling was used to simulate the CT spectral data in Hounsfield units from the 75 keV original CT images to five energy channels from 35keV to 75keV based on the attenuation-energy response curve of three tissue types (i.e. fat, water and cellular tissue). We investigated the classification performance on the five channel spectral data by both deep learning or CNN and Random Forest (RF). Three types of inputs were considered, one is the spectral raw CT images, one is the co-occurrence matrices (CMs) extracted from the CT images, and the third one is the Haralick features (HF) extracted from the CMs. In accordance, three classification model was investigated based on two classifiers of RF and CNN. Experimental results showed the AUC (area under the receiver operating characteristic curve) score was improved by 12.6%, 0.44% and 3.3% for the spectral images, CMs and HFs respectively on the five-energy spectral data comparing to the original 75kev data only. Difference images scheme among neighboring energy channels will further improve the AUC score to 17.4%, 3.6% and 4.5% respectively. Moreover, the CM- and HF-inputs can achieve the best AUC of 0.934 and 0.927.

Purpose: Bayesian theory provides a sound framework for ultralow-dose computed tomography (ULdCT) image reconstruction with two terms for modeling the data statistical property and incorporating a priori knowledge for the image that is to be reconstructed. We investigate the feasibility of using a machine learning (ML) strategy, particularly the convolutional neural network (CNN), to construct a tissue-specific texture prior from previous full-dose computed tomography. Approach: Our study constructs four tissue-specific texture priors, corresponding with lung, bone, fat, and muscle, and integrates the prior with the prelog shift Poisson (SP) data property for Bayesian reconstruction of ULdCT images. The Bayesian reconstruction was implemented by an algorithm called SP-CNN-T and compared with our previous Markov random field (MRF)-based tissue-specific texture prior algorithm called SP-MRF-T. Results: In addition to conventional quantitative measures, mean squared error and peak signal-to-noise ratio, structure similarity index, feature similarity, and texture Haralick features were used to measure the performance difference between SP-CNN-T and SP-MRF-T algorithms in terms of the structure and tissue texture preservation, demonstrating the feasibility and the potential of the investigated ML approach. Conclusions: Both training performance and image reconstruction results showed the feasibility of constructing CNN texture prior model and the potential of improving the structure preservation of the nodule comparing to our previous regional tissue-specific MRF texture prior model.

Pseudoginsenoside-F11 (PF11), an ocotillol-type saponin, has been reported to have anti-inflammatory properties, but the effects of PF11 on acute lung inflammation were unknown. The present study aimed to investigate the protective effects and potential mechanisms of PF11 on lipopolysaccharide (LPS)-induced acute lung injury (ALI) in male BALB/c mice. After being treated with PF11 (3, 10, and 30 mg/kg, intravenous) once a day for 3 consecutive days, the mice were challenged by intratracheal instillation of LPS, and then their lung tissues and bronchoalveolar lavage fluid (BALF) were collected for further analysis. The results showed that PF11 attenuated LPS-induced ALI, with alleviated histopathological damage, decreased lung wet/dry weight ratio, and reduced protein concentration and inflammatory cells number in BALF. Moreover, PF11 reversed the LPS-induced increases of mRNA expression and protein levels of interleukin-6, tumor necrosis factor-α, and interleukin-1β. Meanwhile, PF11 decreased LPS-induced myeloperoxidase activity and neutrophil infiltration in lung tissue by reducing the expression of macrophage inflammatory protein-2 and intercellular adhesion molecule-1, as well as enhanced neutrophil clearance by accelerating neutrophils apoptosis and their phagocytosis by alveolar macrophages. In conclusion, these results indicated that PF11 significantly attenuated LPS-induced ALI through suppressing neutrophil infiltration and accelerating neutrophil clearance, suggesting its potential in the treatment of ALI.

Bayesian theory lies down a sound framework for ultralow-dose computed tomography (ULdCT) image reconstruction with two terms for modeling the data statistical property and incorporating a priori knowledge for the tobe- reconstructed image. This study investigates the feasibility of using machine learning strategy, particularly the convolutional neural network (CNN), to construct a tissue-specific texture prior from previous full-dose CT (FdCT) and integrates the prior with the pre-log shift Poisson (SP) data property for Bayesian reconstruction of ULdCT images. The Bayesian reconstruction was implemented by an algorithm, called SP-CNN-T, and compared with our previous Markov random field (MRF) based tissue-specific texture prior algorithm, called SP-MRF-T. Both training performance and image reconstruction results showed the feasibility of constructing CNN texture prior model and the potential of improving the structure preservation of the nodule comparing to our previous regional tissue-specific MRF texture prior model. Quantitative structure similarity index (SSIM) and texture Haralick features (HF) were used to measure the performance difference between SP-CNN-T and SP-MRF-T algorithms, demonstrating the feasibility and the potential of the investigated machine learning approach.

The accurate identification of malignant polyp on colon CT is critical for the early detection of colorectal cancer, which also offers patients the best chance of cure. Deep learning based methods, especially convolution neural network (CNN) based methods, have been proposed for computer-aided polyp diagnosis due to CNN's strength in feature learning. However, most of the current CNN models focus on the 2D information or use multiple 2D slices as a 2.5D model input, which does not consider the $3D$ spatial information. In this work, we propose a CNN based 3D polyp diagnosis method. The proposed method encodes the $3D$ information into a multi-dimensional gray-level co-occurrence tensor. Each dimension represents one sampling view in the 3D space and 13 dimensions are used in this work. This model takes advantage of the co-occurrence matrix which is a good texture indicator to differentiate the tissue textures between benign and malignant. Additionally, our proposed method solves the problem of input size selection due to huge variants of polyp size and could be extended to other applications. Experiment results demonstrated that our method achieves an AUC of 0.93, which outperforms 2D (AUC 0.57) and 3D (AUC 0.72) convolution neural network solutions and the current state-of-the-art method (AUC 0.86).

Sparse view computed tomography (CT) is an effective way to lower the radiation exposure, but results in streaking artifacts in the constructed CT image due to insufficient projection views. Several approaches have been reported for full view sinogram synthesis by interpolating the missing data into the sparse-view sinogram. However, current interpolation methods tend to generate over-smoothed sinogram, which could not preserve the sharpness of the image. Such sharpness is often referred to the region boundaries or tissue texture and of high importance as clinical indicators. To address this issue, this paper aims to propose an efficient sharpness-preserve spare-view CT sinogram synthesis method based on convolutional neural network (CNN). The sharpness preserving is stressed by the zero-order and first-order difference based loss function in the model. This study takes advantage of the residual design to overcome the problem of degradation for our deep network (20 layers), which is capable of extracting high level information and dealing with large sample dimensions (672 x 672). The proposed model design and loss function achieved a better performance in both quantitative and qualitative evaluation comparing to current state-of-the-art works. This study also performs ablation test on the effect of different designs and researches on hyper-parameter settings in the loss function.

We investigate whether β-carotene, a known natural antioxidant, can reduce oxidative stress induced by traumatic brain injury. In addition, we investigated the underlying mechanism of traumatic brain injury focusing on the NF-E2-related factor (Nrf2) pathway. A controlled cortical impact model was used to mimic traumatic brain injury. Using this model, we evaluated brain edema, lesion volume, neurologic deficits, reactive oxygen species, and the expression of Nrf2-related protein markers. The results of our study demonstrated that cognitive performance and neural functions were improved with β-carotene administration. In addition, β-carotene reduced brain edema and reactive oxygen species levels after traumatic brain injury. Nrf2 nuclear accumulation was increased and was accompanied by decreased Keap1 expression. The expression of quinone oxidoreductase 1, a target gene of the Nrf2 signaling pathway was increased. However, lesion volume was not significantly reduced after β-carotene treatment. Taken together, our data demonstrated that β-carotene administration was neuroprotective and alleviated oxidative stress by modulating the Nrf2/Keap1- mediated antioxidant pathway in the traumatic brain injury model.

Detecting clinically-significant lung nodules, a potential precursor of lung cancer, at as low as achievable radiation dose is demanded to minimize the stochastic radiation effects. This study aims to fulfill these goals by exploring optimal image reconstruction strategies and evaluating these strategies at multiple dose levels with prospective patient studies. Total 133 patients with a suspicious pulmonary nodule scheduled for biopsy, were recruited and data were acquired at 120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images in a random fashion, blinded to the algorithms used for the reconstructions. Each nodule identified in the image volume was marked, (including the 133 biopsy target nodules and 28 other non-target nodules). A 10-point likert scale was used for all scoring to characterize the target nodule images. The score for FBP drops from 100 to 20mAs faster than MRF-H, who drops faster than MRF-T, as expected. All the plots drop faster from 100 to 40 than from 40 to 20mAs. For detection of both the target nodules and the non-target nodules some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively, while MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs.

Polyp classification is a feature selection and clustering process. Picking the most effective features from multiple polyp descriptors without redundant information is a great challenge in this procedure. We propose a multilayer feature selection method to construct an optimized descriptor for polyp classification with a feature-grouping strategy in a hierarchical framework. First, the proposed method makes good use of image metrics, such as intensity, gradient, and curvature, to divide their corresponding polyp descriptors into several feature groups, which are the preliminary units of this method. Then each preliminary unit generates two ranked descriptors, i.e., their optimized variable groups (OVGs) and preliminary classification measurements. Next, a feature dividing-merging (FDM) algorithm is designed to perform feature merging operation hierarchically and iteratively. Unlike traditional feature selection methods, the proposed FDM algorithm includes two steps for feature dividing and feature merging. At each layer, feature dividing selects the OVG with the highest area under the receiver operating characteristic curve (AUC) as the baseline while other descriptors are treated as its complements. In the fusion step, the FDM merges some variables with gains into the baseline from the complementary descriptors iteratively on every layer until the final descriptor is obtained. This proposed model (including the forward step algorithm and the FDM algorithm) is a greedy method that guarantees clustering monotonicity of all OVGs from the bottom to the top layer. In our experiments, all the selected results from each layer are reported by both graphical illustration and data analysis. Performance of the proposed method is compared to five existing classification methods by a polyp database of 63 samples with pathological reports. The experimental results show that our proposed method outperforms other methods by 4% to 23% gains in terms of AUC scores.

Neutrophils have been traditionally considered as the major mediators of harmful inflammatory responses in ischemic stroke, whereas accumulating evidence indicates that neutrophils can be polarized into an N2 phenotype. Similar to M2 microglia, N2 neutrophils contribute to resolution of inflammation and may participate in neuroprotection. However, it remains unclear whether N2 neutrophils protect ischemic neurons and whether they are associated with long-term outcomes after transient cerebral ischemia in rats. The present study proved that N2 neutrophils protected against oxygen glucosedeprivation/re-oxygenation (OGD/R)-induced primary cortical neuron injury via brain-derived neurotrophic factor/tropomyosin-related kinase B (BDNF/TrkB) signaling. In addition, in vivo studies revealed that transient middle cerebral artery occlusion (tMCAO)-induced injury exhibited spontaneous recovery over time in rats. Moreover, neutrophils could infiltrate the ipsilateral brain parenchyma from the periphery after transient cerebral ischemia. Pearson's correlation analysis indicated that the proportion of N2 neutrophils in ipsilateral brain parenchyma was negatively correlated with the number of degenerating neurons, modified Neurological Severity Score (mNSS), brain water content and infarct volume, and positively correlated with the number of surviving neurons and grip strength. In summary, the present study shows that N2 neutrophils likely participate in spontaneous recovery after transient cerebral ischemia by inhibiting ischemic neuron damage in rats, which indicates that N2 neutrophils may represent promising therapeutic target for promoting recovery after ischemic stroke.

A new approach is developed for environment energy harvesting based on an electrostatic MEMS switch due to its hysteresis property. A hysteresis model of the electrostatic MEMS switch is established, and theoretical and experimental results show that the hysteresis window is designable. A demo for piezoelectric energy harvesting based on the MEMS switch is designed and finished. The approach is compacted, passive and efficient, which facilitates the development of energy harvesting.

Poly (N-isopropylacrylamide)-co-(acrylic acid) (pNIPAm-co-AAc) polymer brushes were grown from Au-coated glass substrates via surface initiated atom transfer radical polymerization (SI-ATRP). Subsequently, another thin Au layer was deposited on top of the brush to yield a sandwich structure, with the brush confined between the two Au layers. This structure was shown to exhibit excellent optical properties and shows a response to multiple external stimuli, such as pH, temperature and humidity. This novel device could be used for sensing, biosensing, drug delivery, or for other applications that require light manipulation and wavelength filtration.

A series of pyridine-functionalized poly (N-isopropylacrylamide)-based microgels were synthesized. Subsequently, a structured photonic device was fabricated by sandwiching the microgels between two thin Au layers, and their response to CO2 investigated. We show that devices show an optical response to CO2, which is reversible over many cycles.

Vertical aligned CuO nanowire arrays were synthesized by a simple and cost-effective thermal oxidation method. They act as cores, radial CuO/ZnO core/shell heterostructure nanowire arrays were fabricated by thermal decomposition. Based upon the core/shell nanowire-based all oxide p–n junction, their photovoltaic performance was investigated. A larger open-circuit voltage of 0.445 V and short-circuit current density of 0.89 mA/cm 2 were obtained. The overall power conversion efficiency is 0.17%.

Poly(N-isopropylacrylamide) (pNIPAm) microgel-based etalons were used to fabricate systems that change visual color in response to light exposure. These systems were fabricated by adding pH responsive microgel-based etalons to a solution composed of the photoacid o-nitrobenzaldehyde (o-NBA). Upon exposure of this system to ultraviolet (UV) irradiation, the photoacid released a proton, lowering the pH of the solution. Since the pNIPAm microgel-based etalon was responsive to pH, the etalon changed its optical properties and, hence, visual color. We went on to show that patterned etalons could be fabricated, which only contained pH-responsive microgels in specific regions. These etalons only changed color in the pH-responsive regions, to yield patterns that change color upon UV light exposure. Finally, the color of the etalon was shown to be fully reversible and could be switched multiple times. These unique systems could potentially be used for display technologies, and as a controlled/triggered drug delivery system.

In this submission, pH responsive poly(N-isopropylacrylamide)–based microgels were used to fabricate optical devices (etalons). We demonstrate that the optical properties of the etalons could be manipulated upon the application of an appropriate electric potential between the etalon and a counter electrode, both in an electrolyte solution. An appropriate electrical potential hydrolyzes the water of the electrolyte solution, which subsequently changes the pH of the electrolyte solution. Since solution pH is able to change the solvation state of the microgels, and the etalon's optical properties depend on the microgel's solvation state, the etalon's optical properties can be tuned with an applied electrical potential. We show that the etalon's optical properties (color) is stable for many hours, until an appropriate potential is applied to bring the solution pH back to its initial value. The device's color switching kinetics were also probed, and showed significant color changes on the minute time scale. The dramatic optical property changes coupled with the reversibility of the device's color makes this system potentially useful for display device applications.

Poly (N-isopropylacrylamide-co-acrylic acid) (pNIPAm-co-AAc) microgel-based free-standing etalons have been developed by fabricating etalons on a solid support that has been coated with a sacrificial polymer layer. Therefore, the etalon could be easily removed from the solid substrate upon dissolution of the sacrificial layer. We show that the free-standing etalons retain the optical properties, and the temperature/pH responsivity, of the etalon before its removal from the solid substrate. Furthermore, we show that the free-standing etalons could be transferred to a variety of different substrates, with different chemistries and shapes. We go on to show that the free-standing etalons could be loaded with a small molecule model drug Crystal Violet (CV), transferred to a hydrogel, and triggered to release the CV in a pH-dependent fashion. In the future, these novel free-standing films can be used for sensing, as transferable optical materials, and will be further developed into drug delivery platforms.

Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stages. One of the major goals is to identify malignant from benign lesions. In recent years, machine deep learning (DL), e.g. convolutional neural network (CNN), has shown encouraging classification performance on medical images. However, DL algorithms always need large datasets with ground truth. Yet in the medical imaging field, especially for cancer imaging, it is difficult to collect such large volume of images with pathological information. Therefore, strategies are needed to learn effectively from small datasets via CNN models. To forward that goal, this paper explores two CNN models by focusing extensively on expansion of training samples from two small pathologically proven datasets (colorectal polyp dataset and lung nodule dataset) and then differentiating malignant from benign lesions. Experimental outcomes indicate that even in very small datasets of less than 70 subjects, malignance can be successfully differentiated from benign via the proposed CNN models, the average AUCs (area under the receiver operating curve) of differentiating colorectal polyps and pulmonary nodules are 0.86 and 0.71, respectively. Our experiments further demonstrate that for these two small datasets, instead of only studying the original raw CT images, feeding additional image features, such as the local binary pattern of the lesions, into the CNN models can significantly improve classification performance. In addition, we find that our explored voxel level CNN model has better performance when facing the small and unbalanced datasets.

Co, Cr and Ag three kinds of metal films are deposited by the magnetron sputtering technique. The electron nonequilibrium heat transfer process is studied and the nonequilibrium heat transport time of the electrons of the three kinds of metal films is measured using the femtosecond transient thermal reflection technique, which plays a positive role in solving the problem that the devices are damaged in the processing of the laser heating the metals. The results show that: in the condition of the same pump power of the femtosecond laser, the nonequilibrium heat transport time of the electrons is Ag > Co > Cr. And for the same kind of the thin films, the nonequilibrium heat transport time of the electrons is proportional to the thickness of the films, and is inversely proportional to the power density of the femtosecond laser on the films.

The femtosecond optical pumped transient reflection technique is an effective tool to investigate the ultrafast dynamics in metal thin films. In this paper, both the transient reflectivity response and nonequilibrium relaxation time of electron–phonon interaction in NiFe alloy thin films are studied by using femtosecond laser pump–probe transient reflection experimental technology. The result shows that the heating time of NiFe thin films with different thickness is independent of the thickness of the films, while the thermalization time by electron–phonon relaxation from nonequilibrium state to equilibrium state is proportional to the thickness of the films. In addition, two transient reflectivity signals on glass substrate films are found which is different with previous researches.

When a short pulse of a Q-switched high-power laser hit surface of materials, laser energy would be absorbed rapidly, thus producing explosive vaporization, and almost at the same time a high-pressure shock wave was induced, called laser shock wave. This ultra-high pressure induced by high power laser had been applied in the fields of basic research and engineering application such as inertial confined fusion, laser shock processing, etc. Influence of variable laser power density on laser shock wave was studied in this paper. Influences of variation of laser wavelength and pulse duration on laser shock waves were also discussed in laser shock processing. Piezoelectric Polyvinylidene Fluoride (PVDF) transducer and TDS3052B Digital Oscilloscope were applied in our experiments to measure the piezoelectric wave of PVDF and the corresponding stress wave induced by laser in real time. Differences and relations between laser-induced shockwave and laser pulse in laser shock processing were researched according to the obtained waves, and the detailed analyses of their causes were given based on the formation of laser-induced shock wave. The research results could have important implications for a better understanding of characteristics of laser shock wave and further studies into the interaction between high-power laser and materials.