Spectrum learning for super-resolution tomographic reconstruction.

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information.Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network's ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality.Significance. The proposed SPEAR network's ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.

A Unifying Probabilistic Framework for Partially Labeled Data Learning.

Partially labeled data learning (PLDL), including partial label learning (PLL) and partial multi-label learning (PML), has been widely used in nowadays data science. Researchers attempt to construct different specific models to deal with the different classification tasks for PLL and PML scenarios respectively. The main challenge in training classifiers for PLL and PML is how to deal with ambiguities caused by the noisy false-positive labels in the candidate label set. The state-of-the-art strategy for both scenarios is to perform disambiguation by identifying the ground-truth label(s) directly from the candidate label set, which can be summarized into two categories: 'the identifying method' and 'the embedding method'. However, both kinds of methods are constructed by hand-designed heuristic modeling under considerations like feature/label correlations with no theoretical interpretation. Instead of adopting heuristic or specific modeling, we propose a novel unifying framework called A Unifying Probabilistic Framework for Partially Labeled Data Learning (UPF-PLDL), which is derived from a clear probabilistic formulation, and brings existing research on PLL and PML under one theoretical interpretation with respect to information theory. Furthermore, the proposed UPF-PLDL also unifies 'the identifying method' and 'the embedding method' into one integrated framework, which naturally incorporates the feature and label correlation considerations. Comprehensive experiments on synthetic and real-world datasets for both PLL and PML scenarios clearly demonstrate the superiorities of the derived framework.

Electrospun polycaprolactone nanofibrous membranes loaded with baicalin for antibacterial wound dressing.

Due to the rise in bacterial resistance, the antibacterial extractions from Chinese herbs have been used more frequently for wound care. In this work, baicalin, an extraction from the Chinese herb Scutellaria baicalensis, was utilized as the antibacterial component in the poly(ε-caprolactone)/MXene (PCL/Ti3C2TX) hybrid nanofibrous membranes for wound dressing. The results revealed that the presence of Ti3C2TX aided in the diameter reduction of the electrospun nanofibers. The PCL hybrid membrane containing 3 wt% Ti3C2TX nanoflakes and 5 wt% baicalin exhibited the smallest mean diameter of 210 nm. Meanwhile, the antibacterial tests demonstrated that the PCL ternary hybrid nanofibers containing Ti3C2TX and baicalin exhibited adequate antibacterial activity against the Gram-positive bacterial S. aureus due to the good synergistic effects of Ti3C2TX naoflakes and baicalin. The addition of Ti3C2TX nanoflakes and baicalin could significantly improve the hydrophilicity of the membranes, resulting in the release of baicalin from the nanofibers. In addition, the cytotoxicity of the nanofibers on rat skeletal myoblast L6 cells confirmed their good compatibility with these PCL-based nanofibrous membrances. This work offers a feasible way to prepare antibacterial nanofibrous membranes using Chinese herb extraction for wound dressing applications.

Berberine coated biocomposite hemostatic film based alginate as absorbable biomaterial for wound healing.

In wound treatment, severe bleeding and infection are always primary challenges. Therefore, it is highly desired to develop novel dressing with both hemostatic and antibacterial capability. Herein, a series of biocomposite hemostatic films (BHFs) based alginate/chitosan/collagen-berberine have been prepared and well characterized for further biofunctional study. We have demonstrated that the hemostatic and antibacterial activities were significantly enhanced by calcium/berberine dual-crosslinking system in the film. Through the synergistic effects, BHF-6B exhibited a shorter in vivo clotting and wound healing time than that of commercial dressing in rat tail amputation and full-thickness skin defect models. Additionally, BHF-6B showed excellent bacteriostatic activity with long-term effects. Moreover, hemolysis and cytotoxicity tests in vitro illustrated the prominent biocompatibility of the composite films. Notably, BHF-6B could be degraded quickly and completely in vivo. Overall, the present work indicated that the functionalized BHF-6B has great potential as an absorbable biomaterial for wound treatment.

Berberine-Coated Biomimetic Composite Microspheres for Simultaneously Hemostatic and Antibacterial Performance.

Biomimetic microspheres containing alginate/carboxymethylcellulose/gelatin and coated with 0%, 1%, 3%, and 6% berberine (BACG, BACG-1B, BACG-3B, BACG-6B) were prepared by the oil-in-water emulsion method combined with spray drying. Through a series of physicochemical parameters and determination of hemostatic properties in vitro and in vivo, the results indicated that BACG and BACG-Bs were effective in inducing platelet adhesion/aggregation and promoting the hemostatic potential due to their biomimetic structure and rough surface. In addition, BACG-6B with high berberine proportion presented better hemostatic performance compared with the commercial hemostatic agent compound microporous polysaccharide hemostatic powder (CMPHP). BACG-6B also showed strong antibacterial activity in the in vitro test. The hemolysis test and cytotoxicity evaluation further revealed that the novel composite biomaterials have good hemocompatibility and biocompatibility. Thus, BACG-6B provides a new strategy for developing a due-functional (hemostat/antibacterial) biomedical material, which may have broad and promising applications in the future.

Dictionary learning based image-domain material decomposition for spectral CT.

The potential huge advantage of spectral computed tomography (CT) is that it can provide accurate material identification and quantitative tissue information by material decomposition. However, material decomposition is a typical inverse problem, where the noise can be magnified. To address this issue, we develop a dictionary learning based image-domain material decomposition (DLIMD) method for spectral CT to achieve accurate material components with better image quality. Specifically, a set of image patches are extracted from the mode-1 unfolding of normalized material images decomposed by direct inversion to train a unified dictionary using the K-SVD technique. Then, the DLIMD model is established to explore the redundant similarities of the material images, where the split-Bregman is employed to optimize the model. Finally, more constraints (i.e. volume conservation and the bounds of each pixel within material maps) are integrated into the DLIMD model. Numerical phantom, physical phantom and preclinical experiments are performed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.

Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-Spectral Manifold Learning.

The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm called spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consistency property of HSI, SSMRPE utilizes a new spatial-spectral combined distance (SSCD) to fuse the spatial structure and spectral information for selecting effective spatial-spectral neighbors of HSI pixels. Then, it explores the spatial relationship between each point and its neighbors to adjust the reconstruction weights to improve the efficiency of manifold reconstruction. As a result, the proposed method can extract the discriminant features and subsequently improve the classification performance of HSI. The experimental results on the PaviaU and Salinas hyperspectral data sets indicate that SSMRPE can achieve better classification results in comparison with some state-of-the-art methods.

Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image.

Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are mostly based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.

Spatial-Spectral Cube Matching Frame for Spectral CT Reconstruction.

Spectral computed tomography (CT) reconstructs the same scanned object from projections of multiple narrow energy windows, and it can be used for material identification and decomposition. However, the multi-energy projection dataset has a lower signal-noise-ratio (SNR), resulting in poor reconstructed image quality. To address this thorny problem, we develop a spectral CT reconstruction method, namely spatial-spectral cube matching frame (SSCMF). This method is inspired by the following three facts: i) human body usually consists of two or three basic materials implying that the reconstructed spectral images have a strong sparsity; ii) the same basic material component in a single channel image has similar intensity and structures in local regions. Different material components within the same energy channel share similar structural information; iii) multi-energy projection datasets are collected from the subject by using different narrow energy windows, which means images reconstructed from different energy-channels share similar structures. To explore those information, we first establish a tensor cube matching frame (CMF) for a BM4D denoising procedure. Then, as a new regularizer, the CMF is introduced into a basic spectral CT reconstruction model, generating the SSCMF method. Because the SSCMF model contains an L0-norm minimization of 4D transform coefficients, an effective strategy is employed for optimization. Both numerical simulations and realistic preclinical mouse studies are performed. The results show that the SSCMF method outperforms the state-of-the-art algorithms, including the simultaneous algebraic reconstruction technique, total variation minimization, total variation plus low rank, and tensor dictionary learning.