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Background: Endplate morphology is considered to be one of the influencing factors of cage subsidence after lumbar interbody fusion (LIF). Previous radiographic evaluations on the endplate mostly used sagittal X-ray or MRI. However, there are few studies on the CT evaluation of the endplate and intervertebral space (IVS), especially the evaluation of coronal morphology and its influence on subsidence and fusion after LIF. We aimed to measure and classify the shapes of the endplate and IVS using coronal CT imaging and evaluate the radiographic and clinical outcomes of different shapes of the endplate/IVS following oblique lateral lumbar interbody fusion (OLIF).Methods: A total of 137 patients (average age 59.1 years, including 75 males and 62 females) who underwent L4-5 OLIF combined with anterolateral fixation from June 2018 to June 2020 were included. The endplate concavity depth (ECD) was measured on the preoperative coronal CT image. According to ECD, the endplate was classified as flat (< 2 mm), shallow (2-4 mm), or deep (> 4 mm). The L4-5 IVS was further classified according to endplate type. The disc height (DH), DH changes, subsidence rate, fusion rate, and Oswestry Disability Index (ODI) in different endplate/IVS shapes were evaluated during 1-year follow up.Results: The ECD of L4 inferior endplate (IEP) was significantly deeper than that of L5 superior endplate (SEP) (4.2 ± 1.1 vs 1.6 ± 0.8, P < 0.01). Four types of L4-5 IVS were identified: shallow-shallow (16, 11.7%), shallow-flat (45, 32.9%), deep-shallow (32, 23.4%), and deep-flat (44, 32.1%). A total of 45 (32.9%) cases of cage subsidence were observed. Only one (6.3%) subsidence event occurred in the shallow-shallow group, which was significantly lower than in the other three groups (19 shallow-flat, 6 deep-shallow, and 19 deep-flat) (P < 0.05). Meanwhile, the shallow-shallow group had the highest fusion rate (15, 93.8%) and the highest rate of reach minimal clinically important difference (MCID) ODI among the four types. For a single endplate, the shape of L4 IEP is the main influencing factor of the final interbody fusion rate, and the shallow shape L4 IEP facilitates fusion ( OR = 2.85, p = 0.03). On the other hand, the flat shape L5 SEP was the main risk factor to cage subsidence (OR = 4.36, p < 0.01).Conclusion: The L4-5 IVS is asymmetrical on coronal CT view and tends to be fornix-above and flat-down. The shallow-shallow IVS has the lowest subsidence rate and best fusion result, which is possibly because it has a relatively good degree in matching either the upper or lower interface of the cage and endplates. These findings provide a basis for the further improvements in the design of OLIF cages. [ABSTRACT FROM AUTHOR]

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods. [ABSTRACT FROM AUTHOR]

Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise. [ABSTRACT FROM AUTHOR]

Phase filtering is a key issue in interferometric synthetic aperture radar (InSAR) applications, such as deformation monitoring and topographic mapping. The accuracy of the deformation and terrain height is highly dependent on the quality of phase filtering. Researchers are committed to continuously improving the accuracy and efficiency of phase filtering. Inspired by the successful application of neural networks in SAR image denoising, in this paper we propose a phase filtering method that is based on deep learning to efficiently filter out the noise in the interferometric phase. In this method, the real and imaginary parts of the interferometric phase are filtered while using a scale recurrent network, which includes three single scale subnetworks based on the encoder-decoder architecture. The network can utilize the global structural phase information contained in the different-scaled feature maps, because RNN units are used to connect the three different-scaled subnetworks and transmit current state information among different subnetworks. The encoder part is used for extracting the phase features, and the decoder part restores detailed information from the encoded feature maps and makes the size of the output image the same as that of the input image. Experiments on simulated and real InSAR data prove that the proposed method is superior to three widely-used phase filtering methods by qualitative and quantitative comparisons. In addition, on the same simulated data set, the overall performance of the proposed method is better than another deep learning-based method (DeepInSAR). The runtime of the proposed method is only about 0.043s for an image with a size of 1024 × 1024 pixels, which has the significant advantage of computational efficiency in practical applications that require real-time processing. [ABSTRACT FROM AUTHOR]

[Display omitted] • Asymmetric membranes for helium extraction were fabricated by NIPS technique. • Separation performance of He/N 2 and He/CH 4 exceeded 2019 upper bound. • The selectivity satisfied He recovery from BOG (1–3% He) of LNG plants. • The membranes exhibited excellent anti-aging property and impurity resistance. Helium extraction from natural gas is energy extensive since >95% feed stream needs to be liquefied in multi-cycles. Membrane gas separation is an energy-efficient alternative because of room temperature operation and no phase change. However, the practical application is significantly retarded by the limited permeation flux, which is mainly attributed to the thick selective layer. Herein, asymmetric 6FDA-APAF 0.5 -Cardo 0.5 copolyimide membranes with effective thickness of ∼ 200 nm were prepared by the one-step phase inversion method. The He permeance reached up to 120 GPU, one order magnitude higher than that of the free-standing one. The selectivity was 44 for He/N 2 and 67 for He/CH 4 binary mixtures. The performance surpassed 2019 upper bound and the benchmark membranes for He separation from N 2 or CH 4. Meanwhile, the performance is resistant to the impurities of C 2 H 6 and CO 2 in the natural gas. Together with long-term stability (>200 h), the thin 6FDA-APAF 0.5 -Cardo 0.5 membrane would pave the way to He extraction from natural gas for practical application. [ABSTRACT FROM AUTHOR]

Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are frequently superior to traditional ones. However, most of the existing DL-based methods are purely data-driven and neglect the filtering model, so that they often need to use a large-scale complex architecture to fit the huge training sets. The issue brings a challenge to improve the accuracy of interferometric phase filtering without sacrificing speed. Therefore, we propose a sparse-model-driven network (SMD-Net) for efficient and high-accuracy InSAR phase filtering by unrolling the sparse regularization (SR) algorithm to solve the filtering model into a network. Unlike the existing DL-based filtering methods, the SMD-Net models the physical process of filtering in the network and contains fewer layers and parameters. It is thus expected to ensure the accuracy of the filtering without sacrificing speed. In addition, unlike the traditional SR algorithm setting the spare transform by handcrafting, a convolutional neural network (CNN) module was established to adaptively learn such a transform, which significantly improved the filtering performance. Extensive experimental results on the simulated and measured data demonstrated that the proposed method outperformed several advanced InSAR phase filtering methods in both accuracy and speed. In addition, to verify the filtering performance of the proposed method under small training samples, the training samples were reduced to 10%. The results show that the performance of the proposed method was comparable on the simulated data and superior on the real data compared with another DL-based method, which demonstrates that our method is not constrained by the requirement of a huge number of training samples. [ABSTRACT FROM AUTHOR]

High-resolution wide-swath (HRWS) synthetic aperture radar (SAR) plays an important role in remote sensing observation. However, the motion errors caused by the carrier platform's instability severely degrade the performance of the HRWS SAR imaging. Conventional motion errors compensation methods have two drawbacks, i.e., (1) ignoring the spatial variation of the phase errors of pixels along the range direction of the scene, which leads to lower compensation accuracy, and (2) performing compensation after echo reconstruction, which fails to consider the difference in motion errors between channels, resulting in poor imaging performance in the azimuth direction. In this paper, to overcome these two drawbacks, a high-precision motion errors compensation method based on sub-image reconstruction (SI-MEC) for high-precision HRWS SAR imaging is proposed. The proposed method consists of three steps. Firstly, the motion errors of the platform are estimated by maximizing the intensity of strong points in multiple regions. Secondly, combined with the multichannel geometry, the equivalent phase centers (EPCs) used for sub-images imaging are corrected and the sub-images imaging is performed before reconstruction. Thirdly, the reconstruction is performed by using the sub-images. The proposed method has two advantages, i.e., (1) compensating for the spatially varying phase errors in the range direction, by correcting EPCs, to improve the imaging quality, and (2) compensating for the motion errors of each channel in sub-image imaging before reconstruction, to enhance the imaging quality in the azimuth direction. Moreover, the experimental results are provided to demonstrate that the proposed method outperforms PGA and BP-FMSA. [ABSTRACT FROM AUTHOR]

Most of the existing image segmentation methods have a strong anti-noise ability but are susceptible to the interference in the background, so they are not suitable for 3-D synthetic aperture radar (SAR) image target extraction. Region of interest (ROI) extraction can improve the anti-interference ability of the image segmentation methods. However, the existing ROI extraction method uses the same threshold to process all the images in the data set. This threshold is not optimal for each image. Designed for 3-D SAR image target extraction, we propose an ROI extraction algorithm with adaptive threshold (REAT) to enhance the anti-interference ability of the existing image segmentation methods. The required thresholds in the proposed algorithm are adaptively obtained by the mapping of the image features. Moreover, the proposed algorithm can easily be applied to existing image segmentation methods. The experiments demonstrate that the proposed algorithm significantly enhances the anti-interference ability and computational efficiency of the image segmentation methods. Compared with the existing ROI extraction algorithm, the proposed algorithm improves the dice similarity coefficient by 6.4%. [ABSTRACT FROM AUTHOR]

Multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a vital technique for modern remote sensing. As multichannel SAR systems usually face the problem of azimuth nonuniform sampling resulting in azimuth ambiguity, the conventional reconstruction methods are adopted to obtain the uniformly sampled signal. However, various errors, especially amplitude, phase, and baseline errors, always significantly degrade the performance of the reconstruction methods. To solve this problem, in this paper, a novel sub-image local area minimum entropy reconstruction method (SILAMER) is proposed, which has favorable adaptability to the HRWS SAR system with various errors. First, according to the idea of image domain reconstruction, the sub-images are generated by employing the back-projection algorithm. Then, we proposed an estimation algorithm based on sub-image local area minimum entropy to obtain the optimal reconstruction coefficient and the compensation phase, which can greatly improve the estimation efficiency by using a local area of the sub-image as the input for estimation. Finally, the sub-images are weighted by the optimal estimated reconstruction coefficient and calibrated by the compensation phase to obtain the unambiguous reconstruction image. The experimental results verify the effectiveness of the proposed method. Noticeably, the proposed algorithm has two additional advantages, i.e., (1) it can perform well under the condition of low signal-to-noise ratio (SNR), and (2) it is suitable for the curved trajectory SAR reconstruction. The simulations verify these advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Because of the three-dimensional (3D) imaging scene's sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods. [ABSTRACT FROM AUTHOR]