Optical coherence elastography under homolateral parallel acoustic radiation force excitation for ocular elasticity quantification.

Alteration in the elastic properties of biological tissues may indicate changes in the structure and components. Acoustic radiation force optical coherence elastography (ARF-OCE) can assess the elastic properties of the ocular tissues non-invasively. However, coupling the ultrasound beam and the optical beam remains challenging. In this Letter, we proposed an OCE method incorporating homolateral parallel ARF excitation for measuring the elasticity of the ocular tissues. An acoustic-optic coupling unit was established to reflect the ultrasound beam while transmitting the light beam. The ARF excited the ocular tissue in the direction parallel to the light beam from the same side of the light beam. We demonstrated the method on the agar phantoms, the porcine cornea, and the porcine retina. The results show that the ARF-OCE method can measure the elasticity of the cornea and the retina, resulting in higher detection sensitivity and a more extensive scanning range.

Optical coherence elastography and its applications for the biomechanical characterization of tissues.

The biomechanical characterization of the tissues provides significant evidence for determining the pathological status and assessing the disease treatment. Incorporating elastography with optical coherence tomography (OCT), optical coherence elastography (OCE) can map the spatial elasticity distribution of biological tissue with high resolution. After the excitation with the external or inherent force, the tissue response of the deformation or vibration is detected by OCT imaging. The elastogram is assessed by stress-strain analysis, vibration amplitude measurements, and quantification of elastic wave velocities. OCE has been used for elasticity measurements in ophthalmology, endoscopy, and oncology, improving the precision of diagnosis and treatment of disease. In this article, we review the OCE methods for biomechanical characterization and summarize current OCE applications in biomedicine. The limitations and future development of OCE are also discussed during its translation to the clinic.

The Influence of Coherent Oxide Interfaces on the Behaviors of Helium (He) Ion Irradiated ODS W.

Tungsten (W), as a promising plasma-facing material for fusion nuclear reactors, exhibits ductility reduction. Introducing high-density coherent nano-dispersoids into the W matrix is a highly efficient strategy to break the tradeoff of the strength-ductility performance. In this work, we performed helium (He) ion irradiation on coherent oxide-dispersoids strengthened (ODS) W to investigate the effect of coherent nanoparticle interfaces on the behavior of He bubbles. The results show that the diameter and density of He bubbles in ODS W are close to that in W at low dose of He ion irradiation. The radiation-induced hardening increment of ODS W, being 25% lower than that of pure W, suggests the involvement of the coherent interface in weakening He ion irradiation-induced hardening and emphasizes the potential of coherent nano-dispersoids in enhancing the radiation resistance of W-based materials.

Development and validation of LightGBM algorithm for optimizing of Helicobacter pylori antibody during the minimum living guarantee crowd based gastric cancer screening program in Taizhou, China.

Gastric cancer continues to be a significant health concern in China, with a high incidence rate. To mitigate its impact, early detection and treatment is key. However, conducting large-scale endoscopic gastric cancer screening is not feasible in China. Instead, a more appropriate approach would be to initially screen high-risk groups and follow up with endoscopic testing as needed. We conducted a study on 25,622 asymptomatic participants aged 45-70 years from a free gastric cancer screening program in the Taizhou city government's Minimum Living Guarantee Crowd (MLGC) initiative. Participants completed questionnaires, blood tests, and underwent gastrin-17 (G-17), pepsinogen I and II (PGI and PGII), and H. pylori IgG antibody (IgG) assessments. Using the light gradient boosting machine (lightGBM) algorithm, we developed a predictive model for gastric cancer risk. In the full model, F1 score was 2.66%, precision was 1.36%, and recall was 58.14%. In the high-risk model, F1 score was 2.51%, precision was 1.27%, and recall was 94.55%. Excluding IgG, the F1 score was 2.73%, precision was 1.40%, and recall was 68.62%. We conclude that H. pylori IgG appears to be able to be excluded from the prediction model without significantly affecting its performance, which is important from a health economic point of view. It suggests that screening indicators can be optimized, and expenditures reduced. These findings can have important implications for policymakers, as we can focus resources on other important aspects of gastric cancer prevention and control.

Multi-task generative adversarial network for retinal optical coherence tomography image denoising.

Objective. Optical coherence tomography (OCT) has become an essential imaging modality for the assessment of ophthalmic diseases. However, speckle noise in OCT images obscures subtle but important morphological details and hampers its clinical applications. In this work, a novel multi-task generative adversarial network (MGAN) is proposed for retinal OCT image denoising.Approach. To strengthen the preservation of retinal structural information in the OCT denoising procedure, the proposed MGAN integrates adversarial learning and multi-task learning. Specifically, the generator of MGAN simultaneously undertakes two tasks, including the denoising task and the segmentation task. The segmentation task aims at the generation of the retinal segmentation map, which can guide the denoising task to focus on the retina-related region based on the retina-attention module. In doing so, the denoising task can enhance the attention to the retinal region and subsequently protect the structural detail based on the supervision of the structural similarity index measure loss.Main results. The proposed MGAN was evaluated and analyzed on three public OCT datasets. The qualitative and quantitative comparisons show that the MGAN method can achieve higher image quality, and is more effective in both speckle noise reduction and structural information preservation than previous denoising methods.Significance. We have presented a MGAN for retinal OCT image denoising. The proposed method provides an effective way to strengthen the preservation of structural information while suppressing speckle noise, and can promote the OCT applications in the clinical observation and diagnosis of retinopathy.

HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification.

Automatic and accurate optical coherence tomography (OCT) image classification is of great significance to computer-assisted diagnosis of retinal disease. In this study, we propose a hybrid ConvNet-Transformer network (HCTNet) and verify the feasibility of a Transformer-based method for retinal OCT image classification. The HCTNet first utilizes a low-level feature extraction module based on the residual dense block to generate low-level features for facilitating the network training. Then, two parallel branches of the Transformer and the ConvNet are designed to exploit the global and local context of the OCT images. Finally, a feature fusion module based on an adaptive re-weighting mechanism is employed to combine the extracted global and local features for predicting the category of OCT images in the testing datasets. The HCTNet combines the advantage of the convolutional neural network in extracting local features and the advantage of the vision Transformer in establishing long-range dependencies. A verification on two public retinal OCT datasets shows that our HCTNet method achieves an overall accuracy of 91.56% and 86.18%, respectively, outperforming the pure ViT and several ConvNet-based classification methods.

Rapid estimations of intensity standard deviations for optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) can map microvascular networks and quantify blood flow velocities with high resolution by calculating intensity standard deviations of time-series signals. However, statistical calculations of the standard deviations need much processing time and reduce the analysis efficiency. In this study, we proposed three optimized OCTA algorithms incorporating rapid estimations of the intensity standard deviations, including the range algorithm, the mean absolute error algorithm and the maximum absolute error algorithm. The abilities of the optimized algorithms to quantify the flow velocities were validated by a flow phantom. After a rat cerebral cortex was imaged, the optimized OCTA algorithms were compared with the conventional relative standard deviation algorithm in the metrics of imaging quality and processing time. The results show that the optimized algorithms incorporating rapid estimations of the intensity standard deviations have faster processing speeds with equivalent image quality.

Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation.

Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging region will distort the vasculature projections and decrease the OCTA image quality. Here, we proposed a deep learning-based segmentation method based on a U-Net convolutional neural network to extract the cortical region from the OCT image. The vascular networks were then visualized by three OCTA algorithms. The image quality of the vasculature projections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.

Achieving high strength and ductility in ODS-W alloy by employing oxide@W core-shell nanopowder as precursor.

With excellent creep resistance, good high-temperature microstructural stability and good irradiation resistance, oxide dispersion strengthened (ODS) alloys are a class of important alloys that are promising for high-temperature applications. However, plagued by a nerve-wracking fact that the oxide particles tend to aggregate at grain boundary of metal matrix, their improvement effect on the mechanical properties of metal matrix tends to be limited. In this work, we employ a unique in-house synthesized oxide@W core-shell nanopowder as precursor to prepare W-based ODS alloy. After low-temperature sintering and high-energy-rate forging, high-density oxide nanoparticles are dispersed homogeneously within W grains in the prepared alloy, accompanying with the intergranular oxide particles completely disappearing. As a result, our prepared alloy achieves a great enhancement of strength and ductility at room temperature. Our strategy using core-shell powder as precursor to prepare high-performance ODS alloy has potential to be applied to other dispersion-strengthened alloy systems.

An iterative multi-path fully convolutional neural network for automatic cardiac segmentation in cine MR images.

PURPOSE: Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information may be highly beneficial for segmentation performance improvement. To this end, this paper proposes an iterative multi-path fully convolutional network (IMFCN) to effectively leverage spatial context for automatic cardiac segmentation in cine MR images. METHODS: To effectively leverage spatial context information, the proposed IMFCN explicitly models the interslice spatial correlations using a multi-path late fusion strategy. First, the contextual inputs including both the adjacent slices and the already predicted mask of the above adjacent slice are processed by independent feature-extraction paths. Then, an atrous spatial pyramid pooling (ASPP) module is employed at the feature fusion process to combine the extracted high-level contextual features in a more effective way. Finally, deep supervision (DS) and batch-wise class re-weighting mechanism are utilized to enhance the training of the proposed network. RESULTS: The proposed IMFCN was evaluated and analyzed on the MICCAI 2017 automatic cardiac diagnosis challenge (ACDC) dataset. On the held-out training dataset reserved for testing, our method effectively improved its counterparts that without spatial context and that with spatial context but using an early fusion strategy. On the 50 subjects test dataset, our method achieved Dice similarity coefficient of 0.935, 0.920, and 0.905, and Hausdorff distance of 7.66, 12.10, and 8.80 mm for LV, RV, and MYO, respectively, which are comparable or even better than the state-of-the-art methods of ACDC Challenge. In addition, to explore the applicability to other datasets, the proposed IMFCN was retrained on the Sunnybrook dataset for LV segmentation and also produced comparable performance to the state-of-the-art methods. CONCLUSIONS: We have presented an automatic end-to-end fully convolutional architecture for accurate cardiac segmentation. The proposed method provides an effective way to leverage spatial context in a two-dimensional manner and results in precise and consistent segmentation results.

Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.

Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could make the segmentation of tumors more accurate. This paper investigates CNN-based methods for automated nasopharyngeal carcinoma (NPC) segmentation using computed tomography (CT) and magnetic resonance (MR) images. Specially, a multi-modality convolutional neural network (M-CNN) is designed to jointly learn a multi-modal similarity metric and segmentation of paired CT-MR images. By jointly optimizing the similarity learning error and the segmentation error, the feature learning processes of both modalities are mutually guided. In doing so, the segmentation sub-networks are able to take advantage of the other modality's information. Considering that each modality possesses certain distinctive characteristics, we combine the higher-layer features extracted by a single-modality CNN (S-CNN) and M-CNN to form a combined CNN (C-CNN) for each modality, which is able to further utilize the complementary information of different modalities and improve the segmentation performance. The proposed M-CNN and C-CNN were evaluated on 90 CT-MR images of NPC patients. Experimental results demonstrate that our methods achieve improved segmentation performance compared to their counterparts without multi-modal information fusion and the existing CNN-based multi-modality segmentation methods.

Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut.

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in medical images is an import task for clinical applications, including radiotherapy. However, NPC features large variations in lesion size and shape, as well as inhomogeneous intensities within the tumor and similar intensity to that of nearby tissues, making its segmentation a challenging task. The present study proposes a novel automated NPC segmentation method in magnetic resonance (MR) images by combining a deep convolutional neural network (CNN) model and a 3-dimensional (3D) graph cut-based method in a two-stage manner. First, a multi-view deep CNN-based segmentation method is performed. A voxel-wise initial segmentation is generated by integrating the inferential classification information of three trained single-view CNNs. Instead of directly using the CNN classification results to achieve a final segmentation, the proposed method uses a 3D graph cut-based method to refine the initial segmentation. Specifically, the probability response map obtained using the multi-view CNN method is utilized to calculate the region cost, which represents the likelihood of a voxel being assigned to the tumor or non-tumor. Structure information in 3D from the original MR images is used to calculate the boundary cost, which measures the difference between the two voxels in the 3D neighborhood. The proposed method was evaluated on T1-weighted images from 30 NPC patients using the leave-one-out method. The experimental results demonstrated that the proposed method is effective and accurate for NPC segmentation.

Hot Deformation Behavior and Microstructure Evolution of 14Cr ODS Steel.

Hot deformation tests of 14Cr oxide dispersion strengthened (ODS) steel fabricated by mechanical alloying and hot isostatic pressing (HIP) were performed on a Gleeble-1500D simulator at temperatures ranging from 1050 to 1200 °C with the strain rate range of 0.001−1 s−1. The relationship between the rheological stress and the deformation condition was studied, and a processing map at the true strain of 0.5 was proposed. Microstructure evolution during the deformation process and the effects of deformation conditions on microstructures were also investigated, as well as the stability of nanoparticles. Results show that the 14Cr ODS steel possesses positive strain rate sensitivity. The flow stress increases with the decrease of deformation temperature and the increase of strain rate. The recrystallization process is promoted by the increase of deformation temperature and the reduction of strain rate. Nanoparticles possess excellent stability during the deformation process and are coherent with the matrix.

The Interface Structure of FeSe Thin Film on CaF2 Substrate and its Influence on the Superconducting Performance.

The investigations into the interfaces in iron selenide (FeSe) thin films on various substrates have manifested the great potential of showing high-temperature-superconductivity in this unique system. In present work, we obtain FeSe thin films with a series of thicknesses on calcium fluoride (CaF2) (100) substrates and glean the detailed information from the FeSe/CaF2 interface by using scanning transmission electron microscopy (STEM). Intriguingly, we have found the universal existence of a calcium selenide (CaSe) interlayer with a thickness of approximate 3 nm between FeSe and CaF2 in all the samples, which is irrelevant to the thickness of FeSe layers. A slight Se deficiency occurs in the FeSe layer due to the formation of CaSe interlayer. This Se deficiency is generally negligible except for the case of the ultrathin FeSe film (8 nm in thickness), in which the stoichiometric deviation from FeSe is big enough to suppress the superconductivity. Meanwhile, in the overly thick FeSe layer (160 nm in thickness), vast precipitates are found and recognized as Fe-rich phases, which brings about degradation in superconductivity. Consequently, the thickness dependence of superconducting transition temperature (Tc) of FeSe thin films is investigated and one of our atmosphere-stable FeSe thin film (127 nm) possesses the highest Tconset/Tczero as 15.1 K/13.4 K on record to date in the class of FeSe thin film with practical thickness. Our results provide a new perspective for exploring the mechanism of superconductivity in FeSe thin film via high-resolution STEM. Moreover, approaches that might improve the quality of FeSe/CaF2 interfaces are also proposed for further enhancing the superconducting performance in this system.

Microstructure Refinement in W-Y2O3 Alloy Fabricated by Wet Chemical Method with Surfactant Addition and Subsequent Spark Plasma Sintering.

With the aim of preparing high performance oxide-dispersion-strengthened tungsten based alloys by powder metallurgy, the W-Y2O3 composite nanopowder precursor was fabricated by an improved wet chemical method with anion surfactant sodium dodecyl sulfate (SDS) addition. It is found that the employment of SDS can dramatically decrease W grain size (about 40 nm) and improve the size uniformity. What's more, SDS addition can also remarkably improve the uniform dispersion of Y2O3 particles during the synthesis process. For the alloy whose powder precursor was fabricated by traditional wet chemical method without SDS addition, only a few Y2O3 dispersoids with size of approximate 10-50 nm distribute unevenly within tungsten grains. Nevertheless, for the sintered alloy whose powder precursor was produced by improved wet chemical method, the Y2O3 dispersoids (about 2-10 nm in size) with near spherical shape are dispersed well within tungsten grains. Additionally, compared with the former, the alloy possesses smaller grain size (approximate 700 nm) and higher relative density (99.00%). And a Vickers microhardness value up to 600 Hv was also obtained for this alloy. Based on these results, the employment of SDS in traditional wet chemical method is a feasible way to fabricate high performance yttria-dispersion-strengthened tungsten based alloys.

Improvement in the transport critical current density and microstructure of isotopic Mg11B2 monofilament wires by optimizing the sintering temperature.

Superconducting wires are widely used in fabricating magnetic coils in fusion reactors. In consideration of the stability of 11B against neutron irradiation and lower induced radio-activation properties, MgB2 superconductor with 11B serving as boron source is an alternative candidate to be used in fusion reactor with severe irradiation environment. In present work, a batch of monofilament isotopic Mg11B2 wires with amorphous 11B powder as precursor were fabricated using powder-in-tube (PIT) process at different sintering temperature, and the evolution of their microstructure and corresponding superconducting properties was systemically investigated. Accordingly, the best transport critical current density (Jc) = 2 × 104 A/cm2 was obtained at 4.2 K and 5 T, which is even comparable to multi-filament Mg11B2 isotope wires reported in other work. Surprisingly, transport Jc vanished in our wire which was heat-treated at excessively high temperature (800 °C). Combined with microstructure observation, it was found that lots of big interconnected microcracks and voids that can isolate the MgB2 grains formed in this whole sample, resulting in significant deterioration in inter-grain connectivity. The results can be a constructive guide in fabricating Mg11B2 wires to be used as magnet coils in fusion reactor systems such as ITER-type tokamak magnet.

Improved Superconducting properties in the Mg(11)B2 low activation superconductor prepared by low-temperature sintering.

Mg(11)B2 has a great application prospect in the superconducting coils for fusion reactor as the "low activation superconductors". The un-doped Mg(11)B2 and Cu-doped Mg(11)B2 bulks using (11)B as a boron precursor were fabricated by low-temperature sintering in present work. It was found that the prepared Mg(11)B2 low activation superconductors exhibit better Jc performance than all of other Mg(11)B2 samples reported in previous studies. As for Cu doped Mg(11)B2, minor Cu addition can obviously improve the Mg(11)B2 grain crystallization and reduce the amount of MgO impurity. Hence, improved grain connectivity and higher Jc at low fields is obtained in Cu doped Mg(11)B2 samples. For un-doped samples, refined grains and more MgO impurity with proper size brought about more flux pinning centers, resulting in better Jc performance at high fields.

Tuning Superconductivity in FeSe Thin Films via Magnesium Doping.

In contrast to its bulk crystal, the FeSe thin film or layer exhibits better superconductivity performance, which recently attracted much interest in its fundamental research as well as in potential applications around the world. In the present work, tuning superconductivity in FeSe thin films was achieved by magnesium-doping technique. Tc is significantly enhanced from 10.7 K in pure FeSe films to 13.4 K in optimized Mg-doped ones, which is approximately 1.5 times higher than that of bulk crystals. This is the first time achieving the enhancement of superconducting transition temperature in FeSe thin films with practical thickness (120 nm) via a simple Mg-doping process. Moreover, these Mg-doped FeSe films are quite stable in atmosphere with Hc2 up to 32.7 T and Tc(zero) up to 12 K, respectively, implying their outstanding potential for practical applications in high magnetic fields. It was found that Mg enters the matrix of FeSe lattice, and does not react with FeSe forming any other secondary phase. Actually, Mg first occupies Fe-vacancies, and then substitutes for some Fe in the FeSe crystal lattices when Fe-vacancies are fully filled. Simultaneously, external Mg-doping introduces sufficient electron doping and induces the variation of electron carrier concentration according to Hall coefficient measurements. This is responsible for the evolution of superconducting performance in FeSe thin films. Our results provide a new strategy to improve the superconductivity of 11 type Fe-based superconductors and will help us to understand the intrinsic mechanism of this unconventional superconducting system.

The formation of nano-layered grains and their enhanced superconducting transition temperature in Mg-doped FeSe0.9 bulks.

To search a proper dopant to further improve superconductivity in 11 type Fe-based superconductors makes sense to both their superconductivity mechanism and possible technological applications. In present work, Mg doped FeSe polycrystalline bulks were obtained by a two-step solid-state reaction method. Even though there are many MgSe and iron impurities existing in the Mg heavy doped FeSe bulks, they exhibit obviously increased Tc compared to undoped FeSe sample. It was found that Mg addition has little effect on the crystal lattice parameters of superconducting ß-FeSe, whereas leads to the formation of nano-layered grain structure consisted of MgSe and ß-FeSe with similar X-ray diffraction characteristics. Lots of nano-structural interfaces between FeSe and MgSe formed in this homogenous layered grain structure have significant effect on the superconducting properties and are responsible for the enhancement of Tc, as like the case of FeSe thin film on some specific substrates. Our work not only demonstrates a powerful way for raising Tc in bulk superconductors, but also provides a well-defined platform for systematic studies of the mechanism of unconventional superconductivity by considering interface effect.