Three-dimensional reconstruction of granular porous media based on deep generative models.

Reconstruction of microstructure in granular porous media, which can be viewed as granular assemblies, is crucial for studying their characteristics and physical properties in various fields concerned with the behavior of such media, including petroleum geology and computational materials science. In spite of the fact that many existing studies have investigated grain reconstruction, most of them treat grains as simplified individuals for discrete reconstruction, which cannot replicate the complex geometrical shapes and natural interactions between grains. In this work, a hybrid generative model based on a deep-learning algorithm is proposed for high-quality three-dimensional (3D) microstructure reconstruction of granular porous media from a single two-dimensional (2D) slice image. The method extracts 2D prior information from the given image and generates the grain set as a whole. Both a self-attention module and effective pattern loss are introduced in a bid to enhance the reconstruction ability of the model. Samples with grains of varied geometrical shapes are utilized for the validation of our method, and experimental results demonstrate that our proposed approach can accurately reproduce the complex morphology and spatial distribution of grains without any artificiality. Furthermore, once the model training is complete, rapid end-to-end generation of diverse 3D realizations from a single 2D image can be achieved.

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction.

The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of 0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings.

Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding.

Despite the fact that Versatile Video Coding (VVC) achieves a superior coding performance to High-Efficiency Video Coding (HEVC), it takes a lot of time to encode video sequences due to the high computational complexity of the tools. Among these tools, Multiple Transform Selection (MTS) require the best of several transforms to be obtained using the Rate-Distortion Optimization (RDO) process, which increases the time spent video encoding, meaning that VVC is not suited to real-time sensor application networks. In this paper, a low-complexity multiple transform selection, combined with the multi-type tree partition algorithm, is proposed to address the above issue. First, to skip the MTS process, we introduce a method to estimate the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units' (sub-CUs') information entropy under binary splitting. When the sum of the RD costs of sub-CUs is greater than or equal to their parent CU, the RD checking of MTS will be skipped. Second, we make full use of the coding information of neighboring CUs to terminate MTS early. The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).

Three-dimensional multiscale fusion for porous media on microtomography images of different resolutions.

Accurately acquiring the three-dimensional (3D) image of a porous medium is an imperative issue for the prediction of multiple physical properties. Considering the inherent nature of the multiscale pores contained in porous media such as tight sandstones, to completely characterize the pore structure, one needs to scan the microstructure at different resolutions. Specifically, low-resolution (LR) images cover a larger field of view (FOV) of the sample, but are lacking small-scale features, whereas high-resolution (HR) images contain ample information, but sometimes only cover a limited FOV. To address this issue, we propose a method for fusing the spatial information from a two-dimensional (2D) HR image into a 3D LR image, and finally reconstructing an integrated 3D structure with added fine-scale features. In the fusion process, the large-scale structure depicted by the 3D LR image is fixed as background and the 2D image is utilized as training image to reconstruct a small-scale structure based on the background. To assess the performance of our method, we test it on a sandstone scanned with low and high resolutions. Statistical properties between the reconstructed image and the target are quantitatively compared. The comparison indicates that the proposed method enables an accurate fusion of the LR and HR images because the small-scale information is precisely reproduced within the large one.

Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks.

Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images-a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produce satisfactory HR results on real-world rock MCT images. One of the main reasons for this is that paired images are usually needed to learn the relationship between LR and HR rock images. However, it is challenging to collect such a dataset in a real scenario. Meanwhile, the simulated datasets may be unable to accurately reflect the model in actual applications. To address these problems, we propose a cycle-consistent generative adversarial network (CycleGAN)-based SR approach for real-world rock MCT images, namely, SRCycleGAN. In the off-line training phase, a set of unpaired rock MCT images is used to train the proposed SRCycleGAN, which can model the mapping between rock MCT images at different resolutions. In the on-line testing phase, the resolution of the LR input is enhanced via the learned mapping by SRCycleGAN. Experimental results show that the proposed SRCycleGAN can greatly improve the quality of simulated and real-world rock MCT images. The HR images reconstructed by SRCycleGAN show good agreement with the targets in terms of both the visual quality and the statistical parameters, including the porosity, the local porosity distribution, the two-point correlation function, the lineal-path function, the two-point cluster function, the chord-length distribution function, and the pore size distribution. Large FOV and HR rock MCT images can be obtained with the help of SRCycleGAN. Hence, this work makes it possible to generate HR rock MCT images that exceed the limitations of imaging systems on FOV and resolution.

Tetra-kis(µ-propanoato-κO:O')bis-[(1,10-phenanthroline-κN,N')(propanoato-κO,O')samarium(III)].

The title complex, [Sm(2)(C(3)H(5)O(2))(6)(C(12)H(8)N(2))(2)], is a dinuclear centrosymmetric mol-ecule, in which two crystallographically equivalent Sm atoms, separated by 3.9502 (2) Å, are bridged by four propanoate anions. Each Sm atom is coordinated by two N atoms from one chelating phenanthroline ligand and seven carboxylate O atoms from five propanoate anions, to form a distorted tricapped trigonal prism.

Microbubbles assist goat liver ablation by high intensity focused ultrasound.

High intensity focused ultrasound (HIFU) has been introduced to treat cancers. However, this therapy is a time-consuming procedure; destructing a deeper volume is also difficult as ultrasonic energy attenuates exponentially with increasing depth in tissues. The aim of the present study was to investigate the effects of introducing microbubbles on liver HIFU ablation. Seventeen goats were divided into groups A (n=8) and B (n=9). The livers in both groups were ablated using HIFU (1.0 MHz, 22,593 W/cm2) performed in the manner of a clinical regime using a clinical device. A microbubble agent was bolus-injected intravenously before HIFU exposure in group B. All animals in group A and seven goats in group B were euthanased to evaluate the ablation efficiency 24 h after HIFU. The necrosis rate (mm3/s), which was the volume of necrosis tissue per second of HIFU exposure, was used to judge the ablation efficiency. Pathological examinations were performed to determine whether there were residual intact tissues within the exposed volume. The other two goats in group B were used to determine the delayed pathological changes 7 days after ultrasonic ablation. The necrosis rate (mm3/s) was increased in group B (14.4647+/-4.1960 versus 33.5302+/-12.4484, P=0.0059). Pathological examinations confirmed that there were no residual unaffected tissue focuses within the exposed volume. Two remarkable changes occurred in the other two goats in group B 7 days after HIFU: there were ghost-cell islands at the periphery of the ablated tissues, and surrounding adjacent tissues outside the reactive zone necrotized. These findings showed that microbubbles could be used to assist liver HIFU ablation.

The use of a micro-bubble agent to enhance rabbit liver destruction using high intensity focused ultrasound.

Liver tissues in New Zealand rabbits were ablated using high intensity focused ultrasound (HIFU, 14300 W/cm(2), 1.0 MHz). The animals were intravenously administered 0.2 ml of micro-bubble agent in the experimental (n=20) group and an isovolumetric normal saline solution in the control (n=27) group before HIFU treatment which was performed as a linear scan. In both groups, the preselected tissue volumes were destroyed without harming the overlying tissues. Necrosis rate (NR, cm(3)/s) was used to reflect the ablation efficiency, which was the tissue volume of occurring coagulative necrosis per 1s HIFU exposure. NR was improved in the experimental group (0.0570+/-0.0433 vs 0.0120+/-0.0122, P=0.0002). Pathological studies confirmed that there were no residual intact targets within the exposed volume. These findings suggested that the introduction of the micro-bubble agent enhanced HIFU liver destruction.