Analysis of CT imaging changes of psoas major muscles in patients with lumbar disc herniation mainly based on low back pain and lower limb pain

BackgroundThe study aimed to compare the area changes of CT (computed tomograghy) imaging of psoas major muscle (PM) in patients with lumbar disc herniation (LDH) mainly based on low back pain (LBP) and lower limb pain (LLP), and to analyze the correlation among them.MethodsWe retrospectively analyzed the lumbar CT imaging data of 120 patients with LDH and 60 healthy control people in our hospital from July 2017 to August 2019. They were divided into LBP group (60 cases), LLP group (60 cases) and healthy controls group (60 cases). According to the pain duration and pain degree, LBP group and LLP group were divided into three subgroups respectively. The maximum cross-sectional area (CSA) of PM and the CSA of L5 vertebral body were calculated by Image J software, and the ratio of them was the maximum CSA index of PM. The maximum CSA indices of PM among three groups and three subgroups were compared, respectively.ResultsThe baseline data among the three groups weren’t significantly different (P > 0.05), yet the maximum CSA index of PM did (P < 0.05). In the LBP group, the maximum CSA indices of PM among the three subgroups (short, medium and long) according to the pain duration were significantly different (P < 0.05), and those among the three subgroups (light, medium and heavy) according to pain degree did (P < 0.05). In the LLP group, the maximum CSA indices of PM among the three subgroups (short, medium and long) were compared, but there was not statistical difference among the three subgroups (P > 0.05). No statistical difference in terms of the maximum CSA indices of PM among the three subgroups (light, medium and heavy) was observed (P > 0.05).ConclusionThe atrophy and thinning of PM may be related to LDH. The correlation between the atrophy of PM and LBP was greater than that of LLP. The atrophy of PM in LDH patients with LBP increased with the prolongation of pain duration and aggravation of pain degree

Learn Single-horizon Disease Evolution for Predictive Generation of Post-therapeutic Neovascular Age-related Macular Degeneration

Most of the existing disease prediction methods in the field of medical image processing fall into two classes, namely image-to-category predictions and image-to-parameter predictions. Few works have focused on image-to-image predictions. Different from multi-horizon predictions in other fields, ophthalmologists prefer to show more confidence in single-horizon predictions due to the low tolerance of predictive risk. We propose a single-horizon disease evolution network (SHENet) to predictively generate post-therapeutic SD-OCT images by inputting pre-therapeutic SD-OCT images with neovascular age-related macular degeneration (nAMD). In SHENet, a feature encoder converts the input SD-OCT images to deep features, then a graph evolution module predicts the process of disease evolution in high-dimensional latent space and outputs the predicted deep features, and lastly, feature decoder recovers the predicted deep features to SD-OCT images. We further propose an evolution reinforcement module to ensure the effectiveness of disease evolution learning and obtain realistic SD-OCT images by adversarial training. SHENet is validated on 383 SD-OCT cubes of 22 nAMD patients based on three well-designed schemes based on the quantitative and qualitative evaluations. Compared with other generative methods, the generative SD-OCT images of SHENet have the highest image quality. Besides, SHENet achieves the best structure protection and content prediction. Qualitative evaluations also demonstrate that SHENet has a better visual effect than other methods. SHENet can generate post-therapeutic SD-OCT images with both high prediction performance and good image quality, which has great potential to help ophthalmologists forecast the therapeutic effect of nAMD

Label Adversarial Learning for Skeleton-level to Pixel-level Adjustable Vessel Segmentation

You can have your cake and eat it too. Microvessel segmentation in optical coherence tomography angiography (OCTA) images remains challenging. Skeleton-level segmentation shows clear topology but without diameter information, while pixel-level segmentation shows a clear caliber but low topology. To close this gap, we propose a novel label adversarial learning (LAL) for skeleton-level to pixel-level adjustable vessel segmentation. LAL mainly consists of two designs: a label adversarial loss and an embeddable adjustment layer. The label adversarial loss establishes an adversarial relationship between the two label supervisions, while the adjustment layer adjusts the network parameters to match the different adversarial weights. Such a design can efficiently capture the variation between the two supervisions, making the segmentation continuous and tunable. This continuous process allows us to recommend high-quality vessel segmentation with clear caliber and topology. Experimental results show that our results outperform manual annotations of current public datasets and conventional filtering effects. Furthermore, such a continuous process can also be used to generate an uncertainty map representing weak vessel boundaries and noise

Design and Implementation of a Control Strategy for Microgrid Containing Renewable Energy Generations and Electric Vehicles

Large amount of such renewable energy generations as wind/photovoltaic generations directly connected to grid acting as distributed generations will cause control, protection, security, and safety problems. Microgrid, which has advantages in usage and control of distributed generations, is a promising approach to coordinate the conflict between distributed generations and the grid. Regarded as mobile power storages, batteries of electric vehicles can depress the fluctuation of power through the point of common coupling of microgrid. This paper presents a control strategy for microgrid containing renewable energy generations and electric vehicles. The control strategy uses current control for renewable energy generations under parallel-to-grid mode, and uses master-slave control under islanding mode. Simulations and laboratory experiments prove that the control strategy works well for microgrid containing renewable energy generations and electric vehicles and provides maximum power output of renewable energy and a stable and sustainable running under islanding mode

Decoupling Techniques for Coupled PDE Models in Fluid Dynamics

We review decoupling techniques for coupled PDE models in fluid dynamics. In particular, we are interested in the coupled models for fluid flow interacting with porous media flow and the fluid structure interaction (FSI) models. For coupled models for fluid flow interacting with porous media flow, we present decoupled preconditioning techniques, two-level and multilevel methods, Newton-type linearization-based two-level and multilevel algorithms, and partitioned time-stepping methods. The main theory and some numerical experiments are given to illustrate the effectiveness and efficiency of these methods. For the FSI models, partitioned time-stepping algorithms and a multirate time-stepping algorithm are carefully studied and analyzed. Numerical experiments are presented to highlight the advantages of these methods

Cross-View Hierarchy Network for Stereo Image Super-Resolution

Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. To attain superior performance, many methods have prioritized designing complex modules to fuse similar information across views, yet overlooking the importance of intra-view information for high-resolution reconstruction. It also leads to problems of wrong texture in recovered images. To address this issue, we explore the interdependencies between various hierarchies from intra-view and propose a novel method, named Cross-View-Hierarchy Network for Stereo Image Super-Resolution (CVHSSR). Specifically, we design a cross-hierarchy information mining block (CHIMB) that leverages channel attention and large kernel convolution attention to extract both global and local features from the intra-view, enabling the efficient restoration of accurate texture details. Additionally, a cross-view interaction module (CVIM) is proposed to fuse similar features from different views by utilizing cross-view attention mechanisms, effectively adapting to the binocular scene. Extensive experiments demonstrate the effectiveness of our method. CVHSSR achieves the best stereo image super-resolution performance than other state-of-the-art methods while using fewer parameters. The source code and pre-trained models are available at https://github.com/AlexZou14/CVHSSR.Comment: 10 pages, 7 figures, CVPRW, NTIRE202

Quantum Algorithm for Unsupervised Anomaly Detection

Anomaly detection, an important branch of machine learning, plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. As one of the most commonly used unsupervised anomaly detection algorithms, the Local Outlier Factor algorithm (LOF algorithm) has been extensively studied. This algorithm contains three steps, i.e., determining the k-distance neighborhood for each data point x, computing the local reachability density of x, and calculating the local outlier factor of x to judge whether x is abnormal. The LOF algorithm is computationally expensive when processing big data sets. Here we present a quantum LOF algorithm consisting of three parts corresponding to the classical algorithm. Specifically, the k-distance neighborhood of x is determined by amplitude estimation and minimum search; the local reachability density of each data point is calculated in parallel based on the quantum multiply-adder; the local outlier factor of each data point is obtained in parallel using amplitude estimation. It is shown that our quantum algorithm achieves exponential speedup on the dimension of the data points and polynomial speedup on the number of data points compared to its classical counterpart. This work demonstrates the advantage of quantum computing in unsupervised anomaly detection