Multi-energy CT material decomposition using graph model improved CNN.

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications.

Preparation and molecular simulation of an environmentally friendly dust-fixing agent based on chitosan-gelatin.

In order to solve the hazard of coal mine dust, a dust-fixing agent (GC-TG-JFC) was prepared with gelatin, chitosan, octadecanol polyoxyethylene ether, and glutamine transaminase. The experimental conditions and the formulation were optimized by response surface method. The ratio of gelatin, chitosan, octadecanol polyoxyethylene ether, and glutamine transaminase was 0.405:0.211:0.095:0.286 and the dilution ratio was 1:30. The results of product performance test showed that the dust fixation rate could reach 99.95% when the wind speed was 9 m/s. The viscosity of the diluted solution was 42.5 mPa·s. The Forcite module in Materials studio software was used to analyze and calculate the radial distribution concentration, diffusion coefficient, and binding energy of the solution. The results showed that GC-TG-JFC migrated more water molecules to the surface of coal through the action of van der Waals force and hydrogen bond. In addition, the binding energy of water molecules increased and the diffusion coefficient decreased, which improved the binding ability of water molecules with coal. It could be found that GC-TG-JFC had good dust fixation performance by combining experiment and molecular dynamics method.

A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.

PURPOSE: X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semi-supervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. METHODS: The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squared-error (MSE) loss and perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. RESULTS: The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. The ablation experiment shows that adding real-artifact CT images greatly improves the generalization ability of the network. CONCLUSIONS: The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network.