Super-resolution reconstruction of ultrasound image using a modified diffusion model.

OBJECTIVE: This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the Diffusion Model for Ultrasound Image Super-Resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging. APPROACH: DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation. MAIN RESULTS: Experiments were conducted at 2×, 4×, and 8× magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity (LPIPS), with a significant decrease of over 50% at all three magnification factors (2×, 4×, and 8×), as well as improvements in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR's SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation. SIGNIFICANCE: This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

Diagnosis of cognitive and motor disorders levels in stroke patients through explainable machine learning based on MRI.

BACKGROUND: Globally, stroke is the third most significant cause of disability. A stroke may produce motor, sensory, perceptual, or cognitive disorders that result in disability and affect the likelihood of recovery, affecting a person's ability to function. Evaluation post-stroke is critical for optimal stroke care. PURPOSE: Traditional methods for classifying the clinical disorders of cognitive and motor in stroke patients use assessment and interrogative measures, which are time-consuming, complex, and labor-intensive. In response to the current situation, this study develops an algorithm to automatically classify motor and cognitive disorders in stroke patients by 3D brain MRI to assist physicians in diagnosis. METHODS: First, radiomics and fusion features are extracted from the OAx T2 Propeller of 3D brain MRI. Then, we use 14 machine learning models and one model ensemble method to predict Fugl-Meyer and MMSE levels of stroke patients. Next, we evaluate the models using accuracy, recall, f1-score, and area under the curve (AUC). Finally, we employ SHAP to explain the output of the model. RESULTS: The best predictive models come from Random Forest (RF) Classifier with fusion features in cognitive classification and Linear Discriminant Analysis (LDA) with radiomics features in motor classification. The highest accuracies are 92.0 and 82.5% for cognitive and motor disorders. CONCLUSIONS: MRI brain maps can classify the cognitive and motor disorders of stroke patients. Radiomics features demonstrate its merits. The proposed algorithms with MRI images can efficiently assist physicians in diagnosing the cognitive and motor disorders of stroke patients in clinical practice. Additionally, this lessens labor costs, improves diagnostic effectiveness, and avoids the subjective difference that comes with manual assessment.

Condition control training-based ConVMLP-ResU-Net for semantic segmentation of esophageal cancer in 18F-FDG PET/CT images.

The precise delineation of esophageal gross tumor volume (GTV) on medical images can promote the radiotherapy effect of esophagus cancer. This work is intended to explore effective learning-based methods to tackle the challenging auto-segmentation problem of esophageal GTV. By employing the progressive hierarchical reasoning mechanism (PHRM), we devised a simple yet effective two-stage deep framework, ConVMLP-ResU-Net. Thereinto, the front-end ConVMLP integrates convolution (ConV) and multi-layer perceptrons (MLP) to capture localized and long-range spatial information, thus making ConVMLP excel in the location and coarse shape prediction of esophageal GTV. According to the PHRM, the front-end ConVMLP should have a strong generalization ability to ensure that the back-end ResU-Net has correct and valid reasoning. Therefore, a condition control training algorithm was proposed to control the training process of ConVMLP for a robust front end. Afterward, the back-end ResU-Net benefits from the yielded mask by ConVMLP to conduct a finer expansive segmentation to output the final result. Extensive experiments were carried out on a clinical cohort, which included 1138 pairs of 18F-FDG positron emission tomography/computed tomography (PET/CT) images. We report the Dice similarity coefficient, Hausdorff distance, and Mean surface distance as 0.82 ± 0.13, 4.31 ± 7.91 mm, and 1.42 ± 3.69 mm, respectively. The predicted contours visually have good agreements with the ground truths. The devised ConVMLP is apt at locating the esophageal GTV with correct initial shape prediction and hence facilitates the finer segmentation of the back-end ResU-Net. Both the qualitative and quantitative results validate the effectiveness of the proposed method.

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

Automatic detection of A-line in lung ultrasound images using deep learning and image processing.

BACKGROUND: Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A-line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A-line in LUS image is important. PURPOSE: In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-line detection method for LUS images with different probe types in this paper. METHODS: First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image-processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A-line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five-fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image-processing-based A-line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. RESULTS: After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R-CNN model. The accuracy, sensitivity, and specificity of A-line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two-stage experiment (pleural line localization and A-line detection) was calculated as the final accuracy of the whole A-line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. CONCLUSIONS: The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clinical diagnosis.

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron.

Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of manual segmentation is low. In order to increase the generalization performance of segmentation model, we proposed a deep learning-based automatic segmentation model called CM-SegNet for segmenting medical images of different modalities. It was designed according to the multiscale input and encoding-decoding thoughts, and composed of multilayer perceptron and convolution modules. This model achieved communication of different channels and different spatial locations of each patch, and considers the edge related feature information between adjacent patches. Thus, it could fully extract global and local image information for the segmentation task. Meanwhile, this model met the effective segmentation of different structural lesion regions in different slices of three-dimensional medical images. In this experiment, the proposed CM-SegNet was trained, validated, and tested using six medical image datasets of different modalities and 5-fold cross validation method. The results showed that the CM-SegNet model had better segmentation performance and shorter training time for different medical images than the previous methods, suggesting it is faster and more accurate in automatic segmentation and has great potential application in clinic.

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best treatment opportunity. Thus, we proposed a deep learning-based method using chest X-ray images of the 28th day of oxygen inhalation for the early severity prediction of bronchopulmonary dysplasia in clinic. METHODS: We first adopted a two-step lung field extraction method by combining digital image processing and human-computer interaction to form the one-to-one corresponding image and label. The designed XSEG-Net model was then trained for segmenting the chest X-ray images, with the results being used for the analysis of heart development and clinical severity. Therein, Six-Point cardiothoracic ratio measurement algorithm based on corner detection was designed for the analysis of heart development; and the transfer learning of deep convolutional neural network models were used for the early prediction of clinical severities. RESULTS: The dice and cross-entropy loss value of the training of XSEG-Net network reached 0.9794 and 0.0146. The dice, volumetric overlap error, relative volume difference, precision, and recall were used to evaluate the trained model in testing set with the result being 98.43 ± 0.39%, 0.49 ± 0.35%, 0.49 ± 0.35%, 98.67 ± 0.40%, and 98.20 ± 0.47%, respectively. The errors between the Six-Point cardiothoracic ratio measurement method and the gold standard were 0.0122 ± 0.0084. The deep convolutional neural network model based on VGGNet had the promising prediction performance, with the accuracy, precision, sensitivity, specificity, and F1 score reaching 95.58 ± 0.48%, 95.61 ± 0.55%, 95.67 ± 0.44%, 96.98 ± 0.42%, and 95.61±0.48%, respectively. CONCLUSIONS: These experimental results of the proposed methods in lung field segmentation, cardiothoracic ratio measurement and clinic severity prediction were better than previous methods, which proved that this method had great potential for clinical application.

Evaluation of Pulmonary Edema Using Ultrasound Imaging in Patients With COVID-19 Pneumonia Based on a Non-local Channel Attention ResNet.

Recent research has revealed that COVID-19 pneumonia is often accompanied by pulmonary edema. Pulmonary edema is a manifestation of acute lung injury (ALI), and may progress to hypoxemia and potentially acute respiratory distress syndrome (ARDS), which have higher mortality. Precise classification of the degree of pulmonary edema in patients is of great significance in choosing a treatment plan and improving the chance of survival. Here we propose a deep learning neural network named Non-local Channel Attention ResNet to analyze the lung ultrasound images and automatically score the degree of pulmonary edema of patients with COVID-19 pneumonia. The proposed method was designed by combining the ResNet with the non-local module and the channel attention mechanism. The non-local module was used to extract the information on characteristics of A-lines and B-lines, on the basis of which the degree of pulmonary edema could be defined. The channel attention mechanism was used to assign weights to decisive channels. The data set contains 2220 lung ultrasound images provided by Huoshenshan Hospital, Wuhan, China, of which 2062 effective images with accurate scores assigned by two experienced clinicians were used in the experiment. The experimental results indicated that our method achieved high accuracy in classifying the degree of pulmonary edema in patients with COVID-19 pneumonia by comparison with previous deep learning methods, indicating its potential to monitor patients with COVID-19 pneumonia.

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model.

Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.

Giant oscillatory Gilbert damping in superconductor/ferromagnet/superconductor junctions.

Interfaces between materials with differently ordered phases present unique opportunities for exotic physical properties, especially the interplay between ferromagnetism and superconductivity in the ferromagnet/superconductor heterostructures. The investigation of zero- and π-junctions has been of particular interest for both fundamental physical science and emerging technologies. Here, we report the experimental observation of giant oscillatory Gilbert damping in the superconducting niobium/nickel-iron/niobium junctions with respect to the nickel-iron thickness. This observation suggests an unconventional spin pumping and relaxation via zero-energy Andreev bound states that exist not only in the niobium/nickel-iron/niobium π-junctions but also in the niobium/nickel-iron/niobium zero-junctions. Our findings could be important for further exploring the exotic physical properties of ferromagnet/superconductor heterostructures and potential applications of ferromagnet π-junctions in quantum computing, such as half-quantum flux qubits.

Evidence for anisotropic spin-triplet Andreev reflection at the 2D van der Waals ferromagnet/superconductor interface.

Fundamental symmetry breaking and relativistic spin-orbit coupling give rise to fascinating phenomena in quantum materials. Of particular interest are the interfaces between ferromagnets and common s-wave superconductors, where the emergent spin-orbit fields support elusive spin-triplet superconductivity, crucial for superconducting spintronics and topologically-protected Majorana bound states. Here, we report the observation of large magnetoresistances at the interface between a quasi-two-dimensional van der Waals ferromagnet Fe0.29TaS2 and a conventional s-wave superconductor NbN, which provides the possible experimental evidence for the spin-triplet Andreev reflection and induced spin-triplet superconductivity at ferromagnet/superconductor interface arising from Rashba spin-orbit coupling. The temperature, voltage, and interfacial barrier dependences of the magnetoresistance further support the induced spin-triplet superconductivity and spin-triplet Andreev reflection. This discovery, together with the impressive advances in two-dimensional van der Waals ferromagnets, opens an important opportunity to design and probe superconducting interfaces with exotic properties.

Automatic identification of cashmere and wool fibers based on the morphological features analysis.

Identification of wool and cashmere extremely similar fibers is always an important topic in the textile industry. In order to solve this problem much better, a novel fiber identification method based on the extraction and analysis of the morphological features was proposed in this paper. Firstly, the original fiber images were captured by the self-developed system including the optical microscope and digital camera. The influence of the acquisition process may lead to the low contrast and impurities, so the original fiber images needed to be processed by the image enhancement and de-noise to obtain the available fiber images with a better quality. Then the hessian matrix of processed images was put into the Frangi filter to detect the edge of the fiber scales, and the binary images of filter output images were processed to obtain the signal-pixel scale skeleton. The connected region labeling algorithm can be adopted for the scale skeleton images to mark and extract every scale from the whole fiber according to the different color information. Next, the three morphological features including scale height, fiber diameter and their ratio can be calculated by the self-defined vertical line rotation analysis method, and the mean value of five different scales was calculated as the final features to describe one fiber. In the experiment, 500 fiber cashmere and 500 wool fiber images were collected for the whole research, and a Bayesian classification model for identifying wool and cashmere fibers was established based on the statistical assumptions of three morphological characteristics. The results show that the identification accuracy of the method proposed in this paper could reached the 94.2%. It also proves that this novel method can be used for the identification of cashmere and wool extremely similar animal fibers.

Nonwovens structure measurement based on NSST multi-focus image fusion.

In the digital optical microscope, the depth of field cannot clearly display all fibers in the same image, due to the thickness of nonwovens. A new multi-focus image fusion algorithm based on non-subsampled shearlet transform (NSST) is proposed to improve the quality of fused image, which realizes the fusion of a series of images taken from the same perspective and makes all fibers clearly within a single image. The rule of large absolute value is used to fuse the high frequency sub-band and the rule of large regional variance is used to fuse the low frequency sub-band. Comparing the method with other methods, the superiority of the method can be seen from several indicators of image quality evaluation. Based on the fused image, the diameter and orientation are measured by Hough transform and image preprocessing, and automatic measurement is realized. The porosity is measured by identifying pores, which is fast and convenient. Experiments show that the measurement of nonwoven fabric structure can be quickly achieved based on image processing.

Probing Magnetism in Insulating Cr2Ge2Te6 by Induced Anomalous Hall Effect in Pt.

Two-dimensional ferromagnet Cr2Ge2Te6 (CGT) is so resistive below its Curie temperature that probing its magnetism by electrical transport becomes extremely difficult. By forming heterostructures with Pt, however, we observe clear anomalous Hall effect (AHE) in 5 nm thick Pt deposited on thin (<50 nm) exfoliated flakes of CGT. The AHE hysteresis loops persist to ∼60 K, which matches well to the Curie temperature of CGT obtained from the bulk magnetization measurements. The slanted AHE loops with a narrow opening indicate magnetic domain formation, which is confirmed by low-temperature magnetic force microscopy (MFM) imaging. These results clearly demonstrate that CGT imprints its magnetization in the AHE signal of the Pt layer. Density functional theory calculations of CGT/Pt heterostructures suggest that the induced ferromagnetism in Pt may be primarily responsible for the observed AHE. Our results establish a powerful way of investigating magnetism in 2D insulating ferromagnets, which can potentially work for monolayer devices.

Investigation of a novel automatic micro image-based method for the recognition of animal fibers based on Wavelet and Markov Random Field.

In the analysis of fiber recognition, the challenge lies in the texture feature extraction. The main aim of this paper is to present a novel texture feature analysis method based on wavelet multi-scale analysis to fully extract texture features of microscopic images resulting in better recognition of similar animal fibers. Thousands of three kinds of similar fiber images including cashmere, sheep wool and goat hair were captured by the optical microscope and the digital camera. They were pre-processed to obtain the enhanced images with background removed. Then the pretreated fiber images were decomposed by 3-layer wavelet transform, four sub-images under the third-layer wavelet decomposition scale were analyzed by Gauss Markov Random Field (GMRF) model and their model parameters were obtained. Through the difference analysis of different kinds of fibers, two model parameters were selected from each sub-image to generate an 8-dimensional feature vectors, which was used to describe the fiber images. The parameters, which were extracted from 1000 images of each kind of fiber, were copied three times and randomly arranged to generate the final data sets. Finally, the data sets were processed by 10-times cross validation method as the training set and testing set of support vector machine (SVM). Ten different recognition rates could be obtained through the experiment, and the mean value was used as the final recognition accuracy of wool and cashmere fibers. The experimental results indicated that the method had a great recognition rate with 90.07% and the performance was robust. It verifies that the method based on wavelet multi-scale analysis is effective for the recognition of similar fibers.

Experimental signatures of spin superfluid ground state in canted antiferromagnet Cr2O3 via nonlocal spin transport.

Spin superfluid is a novel emerging quantum matter arising from the Bose-Einstein condensate (BEC) of spin-1 bosons. We demonstrate the spin superfluid ground state in canted antiferromagnetic Cr2O3 thin film at low temperatures via nonlocal spin transport. A large enhancement of the nonlocal spin signal is observed below ~20 K, and it saturates from ~5 down to 2 K. We show that the spins can propagate over very long distances (~20 µm) in such spin superfluid ground state and that the nonlocal spin signal decreases very slowly as the spacing increases with an inverse relationship, which is consistent with theoretical prediction. Furthermore, spin superfluidity has been investigated in the canted antiferromagnetic phase of the (11[Formula: see text]0)-oriented Cr2O3 film, where the magnetic field dependence of the associated critical temperature follows a 2/3 power law near the critical point. The experimental demonstration of the spin superfluid ground state in canted antiferromagnet will be extremely important for the fundamental physics on the BEC of spin-1 bosons and paves the way for future spin supercurrent devices, such as spin-Josephson junctions.

Role of Oxygen in Ionic Liquid Gating on Two-Dimensional Cr2Ge2Te6: A Non-oxide Material.

Ionic liquid gating can markedly modulate a material's carrier density so as to induce metallization, superconductivity, and quantum phase transitions. One of the main issues is whether the mechanism of ionic liquid gating is an electrostatic field effect or an electrochemical effect, especially for oxide materials. Recent observation of the suppression of the ionic liquid gate-induced metallization in the presence of oxygen for oxide materials suggests the electrochemical effect. However, in more general scenarios, the role of oxygen in the ionic liquid gating effect is still unclear. Here, we perform ionic liquid gating experiments on a non-oxide material: two-dimensional ferromagnetic Cr2Ge2Te6. Our results demonstrate that despite the large increase of the gate leakage current in the presence of oxygen, the oxygen does not affect the ionic liquid gating effect on  the channel resistance of Cr2Ge2Te6 devices (<5% difference), which suggests the electrostatic field effect as the mechanism on non-oxide materials. Moreover, our results show that ionic liquid gating is more effective on the modulation of the channel resistances compared to the back gating across the 300 nm thick SiO2.

Observation of inverse Edelstein effect in Rashba-split 2DEG between SrTiO3 and LaAlO3 at room temperature.

The Rashba physics has been intensively studied in the field of spin orbitronics for the purpose of searching novel physical properties and the ferromagnetic (FM) magnetization switching for technological applications. We report our observation of the inverse Edelstein effect up to room temperature in the Rashba-split two-dimensional electron gas (2DEG) between two insulating oxides, SrTiO3 and LaAlO3, with the LaAlO3 layer thickness from 3 to 40 unit cells (UC). We further demonstrate that the spin voltage could be markedly manipulated by electric field effect for the 2DEG between SrTiO3 and 3-UC LaAlO3. These results demonstrate that the Rashba-split 2DEG at the complex oxide interface can be used for efficient charge-and-spin conversion at room temperature for the generation and detection of spin current.

Spin injection and inverse Edelstein effect in the surface states of topological Kondo insulator SmB6.

There has been considerable interest in exploiting the spin degrees of freedom of electrons for potential information storage and computing technologies. Topological insulators (TIs), a class of quantum materials, have special gapless edge/surface states, where the spin polarization of the Dirac fermions is locked to the momentum direction. This spin-momentum locking property gives rise to very interesting spin-dependent physical phenomena such as the Edelstein and inverse Edelstein effects. However, the spin injection in pure surface states of TI is very challenging because of the coexistence of the highly conducting bulk states. Here, we experimentally demonstrate the spin injection and observe the inverse Edelstein effect in the surface states of a topological Kondo insulator, SmB6. At low temperatures when only surface carriers are present, a clear spin signal is observed. Furthermore, the magnetic field angle dependence of the spin signal is consistent with spin-momentum locking property of surface states of SmB6.