SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration

Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis. The unsupervised deep learning registration method can quickly achieve high registration accuracy without labels. However, these methods generally suffer from uncorrelated features, poor ability to register large deformations and details, and unnatural deformation fields. To address the issues above, we propose an unsupervised multi-scale correlation iterative registration network (SearchMorph). In the proposed network, we introduce a correlation layer to strengthen the relevance between features and construct a correlation pyramid to provide multi-scale relevance information for the network. We also design a deformation field iterator, which improves the ability of the model to register details and large deformations through the search module and GRU while ensuring that the deformation field is realistic. We use single-temporal brain MR images and multi-temporal echocardiographic sequences to evaluate the model's ability to register large deformations and details. The experimental results demonstrate that the method in this paper achieves the highest registration accuracy and the lowest folding point ratio using a short elapsed time to state-of-the-art

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

IntroductionAI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance.MethodsIn this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test.ResultsThe Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different.DiscussionIn this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists

Effect of pyrolysis condition on the adsorption mechanism of heavy metals on tobacco stem biochar in competitive mode

Abstract(#br)To clarify the adsorption mechanism of multi-ions on biochars in competitive environment is very important for the decontamination of co-existed heavy metals. Herein, tobacco stem was pyrolyzed in different temperatures with selected residences to obtain biochars with various surface chemistry. Then the adsorption of co-existed typical heavy-metal ions like lead, cadmium, and copper was studied, followed with systematic analysis of surface properties of the post-adsorption biochars. After carefully examining the adsorption performance and surface property alteration of the demineralized biochars, the adsorption mechanism of multi-ions in competitive environment was discovered. Lead showed the most competitive nature with co-existence of cadmium and copper, but the adsorption..

Electrochemical Hydrogen-Storage Properties of La 0.78

For improving the electrochemical properties of nonstoichiometric AB3-type La0.78Mg0.22Ni2.67Mn0.11Al0.11Co0.52 alloy as negative electrode of Ni-MH battery, its related composites La0.78Mg0.22Ni2.67Mn0.11Al0.11Co0.52-x wt.% M1Ni3.5Co0.6Mn0.4Al0.5 (x = 0, 10, 20, 30) were prepared. Analysis by X-ray diffractometry (XRD) revealed that the composites consist mainly of LaNi5 and La2Ni7 phases. Despite the small decrease in the maximum discharge capacity, the cycle performance was significantly enhanced. Linear polarization (LP), anodic polarization (AP) and potential step discharge experiments revealed that the electrochemical kinetics increases first and then decreases with increasing x

Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning

Cardiovascular disease (CVD) is the most common type of disease and has a high fatality rate in humans. Early diagnosis is critical for the prognosis of CVD. Before using myocardial tissue strain, strain rate, and other indicators to evaluate and analyze cardiac function, accurate segmentation of the left ventricle (LV) endocardium is vital for ensuring the accuracy of subsequent diagnosis. For accurate segmentation of the LV endocardium, this paper proposes the extraction of the LV region features based on the YOLOv3 model to locate the positions of the apex and bottom of the LV, as well as that of the LV region; thereafter, the subimages of the LV can be obtained, and based on the Markov random field (MRF) model, preliminary identification and binarization of the myocardium of the LV subimages can be realized. Finally, under the constraints of the three aforementioned positions of the LV, precise segmentation and extraction of the LV endocardium can be achieved using nonlinear least-squares curve fitting and edge approximation. The experiments show that the proposed segmentation evaluation indices of the method, including computation speed (fps), Dice, mean absolute distance (MAD), and Hausdorff distance (HD), can reach 2.1–2.25 fps, 93.57±1.97%, 2.57±0.89 mm, and 6.68±1.78 mm, respectively. This indicates that the suggested method has better segmentation accuracy and robustness than existing techniques

Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors

WeChat-based vestibular rehabilitation for patients with chronic vestibular syndrome: protocol for a randomised controlled trial

Introduction Dizziness is one of the most common symptoms seen in chronic vestibular syndrome, which has been linked to an increased risk of falls, substantial disability and negative psychological consequences. Recent evidence demonstrated that vestibular rehabilitation therapy (VRT) is effective for treating chronic vestibular symptoms. However, the delivery of VRT remains challenging because of lack of facility, insufficient qualified physiotherapist resources, as well as being in the actual situation of the pandemic. WeChat, the most widely used mobile app in China, offers a more viable way of delivering VRT than traditional office-based approaches do. This study aimed to evaluate the effectiveness of the WeChat-VRT programme for patients with chronic vestibular syndrome.Methods and analysis This is a parallel-group, assessor-blinded randomised controlled trial. Fifty patients who experienced chronic vestibular symptoms longer than 3 months will be randomised into either the WeChat-VRT group or the usual care (UC) group. Participants in the WeChat-VRT group will receive 8-week VRT mainly through the WeChat app. Participants in the UC group will receive once-weekly VRT in the clinic for 8 weeks and remaining time home-based exercise. Outcome assessments will take place at baseline and at the 8th, 12th and 24th weeks after randomisation. The primary outcome will be the change from baseline to the eighth week on the patients’ functional improvements quantified by the Functional Gait Assessment (FGA). The secondary outcomes will include dynamic balance function, emotional well-being, and vestibular activity and participation level. Intention-to-treat analysis will be performed using generalised estimation equation modelling.Ethics and dissemination The trial has been reviewed and approved by the Institutional Review Board of Eye and Ear Nose Throat Hospital of Fudan University (reference number 2017047/1). The study findings will be disseminated via peer-reviewed journals and conferences.Trial registration number ChiCTR2000029457; Pre-results

Effect of Traditional Chinese Herbal Medicine with Antiquorum Sensing Activity on Pseudomonas aeruginosa

Traditional Chinese herbal medicines (TCHMs) were tested for their ability of antiquorum sensing. Water extracts of Rhubarb, Fructus gardeniae, and Andrographis paniculata show antiquorumsensing activity when using Chromobacterium violaceum CV12472 as reporter; the sub-MIC concentrations of these TCHMs were tested against AHL-dependent phenotypic expressions of PAO1. Results showed significant reduction in pyocyanin pigment, protease, elastase production, and biofilm formation in PAO1 without inhibiting the bacterial growth, revealing that the QSI by the extracts is not related to static or killing effects on the bacteria. The results indicate a potential modulation of bacterial cell-cell communication, P. aeruginosa biofilm, and virulence factors by traditional Chinese herbal medicine. This study introduces not only a new mode of action for traditional Chinese herbal medicines, but also a potential new therapeutic direction for the treatment of bacterial infections, which have QSI activity and might be important in reducing virulence and pathogenicity of pathogenic bacteria