Search

This study identifies optimal cooling rates and critical strain thresholds to prevent cracking in Cr12MoV billets during continuous casting through thermoplastic experiments, high‐temperature tensile tests, and a thermal–mechanical coupled simulation model. The result shows that the critical strain (dimensionless) for corner crack propagation at temperatures of 750, 800, and 850 °C are determined to be 0.125, 0.132, and 0.147, respectively. The critical strain for internal crack formation is found to be 0.03. The simulation results indicate that as the billet enters the first straightening roll, the corner strain is 0.035 and the central strain is 0.025. Upon exiting the fourth straightening roll, the corner strain increases to 0.038, while the central strain decreases to 0.021. Through simulation and high‐temperature confocal in situ observation, it can be concluded that a surface cooling rate of ≥5 °C s−1 effectively prevents cracks during the bending and straightening stage of continuous casting. [ABSTRACT FROM AUTHOR]

In chemical production processes, the most suitable operation regime for A-Type powders such as typical FCC particles is high-speed fast fluidization owing to their uniquely advantageous properties. Discrete element method (DEM) for modelling fast fluidization with A-Type powders has rarely been reported. How to appropriately select the DEM time step and the stiffness coefficient is one of the most critical problems for stable and accurate calculation. This article mainly discusses the effect of the stiffness coefficient and DEM time step on simulations of A-type FCC particles. In order to describe the effect of both parameters and their complex interaction, a dimensionless relative DEM time step is introduced. In total, nine cases with different numbers of relative time steps are adopted for modelling a microfluidized bed of A-Type FCC particles, the regime of which is proved to be fast fluidization. Results show that three bifurcations occur in all the simulations. Only the moderate relative time step possesses the capability of modelling the process of particle collision and thus predicts the right flow regime with asymmetric and heterogeneous typical fast fluidized structures. When the relative time step increases to large rank, simulations predict untrue fluidization regimes with symmetric and homogeneous structures. Moreover, both over-large and over-low relative time steps cause excessive particle overlap and thus a divergence of simulation. The further optimization of moderate relative DEM time step in relation to real particle property is unidentifiable and is thus an outstanding issue. That the range of the moderate relative time step is limited indicates that the common soft-sphere model is poor at modelling fast fluidization of A-Type particles. It is suggested that possible future work should be focused on improving the simulation frame and employing the molecular dynamics model to more appropriately deal with particle contact. [ABSTRACT FROM AUTHOR]

This study focuses on the simulation of wet etching of sapphire at different concentrations and temperatures. We use the Evolutionary Kinetic Monte Carlo (E-KMC) method, which combines (i) a removal probability function (RPF), to reduce the large collection of adjustable removal rates to a smaller number of energy parameters, and (ii) an evolutionary algorithm, to optimize those parameters by minimizing errors between simulated and experimental rates for a selected group of surface orientations. To improve the resemblance between simulated and experimental etch rate distributions at different concentrations, additional surface orientations located in the region with worst similarity are used. This is validated by a comparison of experimental and simulated trench profiles obtained on C-plane sapphire wafers with rectangular mask openings. Most importantly, this study addresses for the first time the temperature dependence considered in traditional formulations of the RPF, concluding that it is only partially correct. To overcome this deficiency, a physically meaningful modified removal probability function (M-RPF) is formulated as the product of a standard prefactor and a standard Boltzmann factor, in agreement with Transition State Theory. Effectively, the prefactor transforms the facet-specific orientation-dependence of the etch rate at a given temperature into the atom-specific site-dependence of removal rates on the local neighborhood, while the Boltzmann factor assigns a different activation energy to every surface atom type. It is shown that the M-RPF provides a suitable approach to correctly model the overall temperature dependence. The study extends the range of wet etching applications of the E-KMC method. [2021-0221] [ABSTRACT FROM AUTHOR]

In this paper, the simple, rejection-based kinetic Monte Carlo simulation method is applied for the approximate simulation of the etch rates and three-dimensional etch structures during anisotropic wet etching of sapphire. Based on the analysis of the composition of the atomic structure of sapphire, a model of the simplified atomic structure of sapphire is proposed, which reduces the difficulty of classifying types of surface atoms on the different crystallographic planes. This enables adopting a previously proposed six-index classification method (to differentiate effectively between the various surface atoms of sapphire) and a previously proposed removal probability function (RPF) (to link the removal probability of the surface atom with the configuration of its neighbors). By reducing errors between simulated rates and experimental rates of 11 typical crystallographic planes in the <−1 1 0 0> and <1 1 −2 0> crystallographic zones continuously with an evolutionary algorithm, reasonable values of nine energy parameters of the RPF are obtained. The simulated results describe approximately the experimental counterparts. [ABSTRACT FROM AUTHOR]

In this paper, a model of etch rates of crystallographic planes of sapphire based on step flow mechanism is proposed, which explains the relationship between etch rates of and atomic structures of crystallographic planes accurately. By analyzing the atomic structure of sapphire, a method of identifying step-terrace structures of crystallographic planes is established. Step-terrace structures of crystallographic planes of sapphire can be composed of crystal units (cluster atoms) of terrace types, edge-terrace types and step types by different combinations. Etch rates of crystallographic planes of sapphire can be expressed as functions of removal rates of crystal units. Thus, the relationship between etch rates and atomic structures of crystallographic planes of sapphire is established. The values of removal rates of crystal units can be obtained by the established matrix equations of removal rates of crystal units and experimental etch rates of key crystallographic planes. Under the different experimental conditions, high coincidence between etch rates calculated by the model and experimental etch rates in the $ < -1\,\,1\,\,0\,\,0>$ and $ < 1\,\,1-2\,\,0>$ crystallographic zones is achieved. It proves the validity of the proposed model. [2020-0089] [ABSTRACT FROM AUTHOR]

Excited state unimolecular reactions of some polyenes exhibit localization of their dynamics at a single ethylenic double bond. Here we present studies of the fundamental photophysical processes in the ethylene unit itself. Combined femtosecond time-resolved photoelectron spectroscopy (TRPES) and ab initio quantum chemical calculations was applied to the study of excited state dynamics in cis-butene, trans-butene, trimethylethylene, and tetramethylethylene, following initial excitation to their respective π3s Rydberg states. The wavelength dependence of the π3s Rydberg state dynamics of tetramethylethylene was investigated in more detail. The π3s Rydberg to ππ* valence state decay rate varies greatly with substituent: the 1,2-di- and tri-methyl substituted ethylenes (cis-butene, trans-butene, and trimethylethylene) show an ultrafast decay (∼20 fs), whereas the fully methylated tetramethylethylene shows a decay rate of 2 to 4 orders of magnitude slower. These observations are rationalized in terms of topographical trends in the relevant potential energy surfaces, as found from ab initio calculations: (1) the barrier between the π3s state and the ππ* state increases with increasing methylation, and (2) the π3s/ππ* minimum energy conical intersection displaces monotonically away from the π3s Franck-Condon region with increasing methylation. The use of systematic methylation in combination with TRPES and ab initio computation is emerging as an important tool in discerning the excited state dynamics of unsaturated hydrocarbons. [ABSTRACT FROM AUTHOR]

Photodissociation dynamics of the CH[sub 3] radical at 212.5 nm has been investigated using the H atom Rydberg tagging time-of-flight method with a pure CH[sub 3] radical source generated by the photolysis of CH[sub 3]I at 266 nm. Time-of-flight spectra of the H atom products from the photolysis of both cold and hot methyl radicals have been measured at different photolysis polarizations. Experimental results indicate that the photodissociation of the methyl radical in its ground vibrational state at 212.5 nm excitation occurs on a very fast time scale in comparison with its rotational period, indicating the CH[sub 3] dissociation at 212.5 nm occurs on the excited 3s Rydberg state surface. Experimental evidence also shows that the photodissociation of the methyl radical in the ν[sub 2]=1 state of the umbrella mode at 212.5 nm excitation is characteristically different from that in the ground vibrational state. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Well-dispersion of fillers in polymers is crucial for the preparation of mixed matrix membranes(MMMs). In this paper, COF-LZU1 particles were in-situ prepared in poly(ether- block -amide) membranes for the first time, and the as-obtained MMMs exhibited higher n-butanol/water sorption selectivity and pervaporation selectivity in comparison with that of MMMs prepared by conventional blending methods. The effects of reactants concentrations, growth temperature and solvents on property and pervaporation performance of MMMs were investigated. For the benzene-1,3,5-tricarbaldehyde (TFB) concentration of 2.0 mmol L−1, growth temperature of 70 °C, TFB to p-phenylenediamine molar ratio of 1:1.5, and solvent of n-butanol, the MMMs showed maximum separation factor (22.2) (increased by 139% in comparison with that of pristine membranes) and peameation flux of 611 g m−2 h−1, demonstrating great prospect for the recovery of n-butanol. • COF-LZU1 particles were in-situ prepared in poly(ether- block -amide) membranes. • The membranes exhibited stronger hydrophobicity, higher n-butanol/water sorption and pervaporation selectivity. • The separation factor increased by 139% in comparison with that of pristine membranes. [ABSTRACT FROM AUTHOR]

Abstract Submicron PAF-56 particles with narrow size-distribution were prepared by bottom-up method for the first time, and incorporated in polydimethylsiloxane (PDMS) membranes for pervaporation of 5% n-butanol solution. The effects of PAF-56 loading on the hydrophilicity, swelling degree, partition coefficient and pervaporation performance of the mixed matrix membranes (MMMs) were studied. It was found that, the PAF-56/PDMS MMMs displayed n-butanol sorption-selectivity, and the permeation flux and separation factor increased with the decreased PAF-56 particles size at the same loading, demonstrating size-effects of PAF-56 particles. With the increased PAF-56 loading, the permeation flux and separation factor exhibited maximum values at 2–2.5% loading. For MMMs with PAF-56 size of 0.83 μm and 2.5 wt% loading, the separation factor and permeation fluxes were 48.4 and 1906 g m−2 h−1 (increased by 183% and 66% in comparison with that of pristine PDMS membrane), indicating great potential in recovery of n-butanol. Graphical abstract Image 1 Highlights • Sub-micron PAF-56 particles were prepared by bottom-up method for the first time. • Size-effects of PAF-56 particles were found in pervaporation. • Apparent anti-trade-off effects between permeation flux and separation factor were demonstrated. [ABSTRACT FROM AUTHOR]

Produced by both nature and human activities, sulfur dioxide (SO2) is an important species in the earth's atmosphere. SO2 has also been found in the atmospheres of other planets and satellites in the solar system. The photoabsorption cross sections and photodissociation of SO2 have been studied for several decades. In this paper, we reported the experimental results for photodissociation dynamics of SO2 via the G ̃ 1B1 state. By analyzing the images from the time-sliced velocity map ion imaging method, the vibrational state population distributions and anisotropy parameters were obtained for the O(1D2) + SO(X3Σ−, a1Δ, b1Σ+) and O(1S0) + SO(X3Σ−) channels, and the branching ratios for the channels O(1D2) + SO(X3Σ−), O(1D2) + SO(a1Δ), and O(1D2) + SO(b1Σ+) were determined to be ∼0.3, ∼0.6, and ∼0.1, respectively. The SO products were dominant in electronically and rovibrationally excited states, which may have yet unrecognized roles in the upper planetary atmosphere. [ABSTRACT FROM AUTHOR]

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. [ABSTRACT FROM AUTHOR]

Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain , the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs)/polymer mixed matrix membranes (MMMs) have great potential in pervaporation separation due to the ease of design and modification of MOFs, along with the compatibility between MOFs and polymer matrix. This article reviews the current status of MOFs MMMs for pervaporation, including polymer (hydrophobicity/hydrophilicity, structure stability), MOFs (stability, hydrophobicity/hydrophilicity, surface functional structure, particles morphology and pores size), mass transfer, and applications (dehydration of organic solvents, removal of dilute organic compounds from aqueous streams, separation of organic-organic mixtures, and membrane reactor). The perspectives and suggestions of MOFs MMMs are given. [ABSTRACT FROM AUTHOR]

Multi-atlas segmentation is a powerful approach to automated anatomy delineation via fusing label information from a set of spatially normalized atlases. For simplicity, many existing methods perform pairwise image registration, leading to inaccurate segmentation especially when shape variation is large. In this paper, we propose a dynamic tree-based strategy for effective large-deformation registration and multi-atlas segmentation. To deal with local minima caused by large shape variation, coarse estimates of deformations are first obtained via alignment of automatically localized landmark points. The dynamic tree capturing the structural relationships between images is then employed to further reduce misalignment errors. Evaluation based on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy. [ABSTRACT FROM AUTHOR]

Most of the mixed matrix membranes (MMMs) display the trade-off effect between flux and selectivity in pervaporation, and a strategy for breaking the effect has not been identified. In this paper, MIL-53(Al)-NH 2 nanocrystals were modified with formic acid, valeric anhydride and heptanoic anhydride, and then incorporated into poly(vinyl alcohol) (PVA) membranes for use in the pervaporation of 92.5 wt% ethanol. The MMMs properties (hydrophilicity, swelling degree) and pervaporation performance were successfully adjusted. With the increased hydrophobic constants of surface substitutes, the ethanol permeance rises while the water permeance and the selectivity drops. The pervaporation performances can be tuned from trade-off to anti-trade off through surface modification of MOFs. The synergistic effect of MOFs and polymer matrix is crucial in breaking the trade-off effects. Compared with the pristine PVA membranes, the optimal MIL-53-NHCOH/PVA MMMs (4 wt% loading) display an 206% increase in water permeance, and an 200% increase in selectivity, and the optimal MIL-53-NHCOC 4 H 9 /PVA MMMs (7.5 wt% loading) display a 340 % increase in water permeance, and a 170 % increase in selectivity, demonstrating their great potential in ethanol dehydration. [ABSTRACT FROM AUTHOR]

Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results. [ABSTRACT FROM AUTHOR]

Recently, multi-atlas patch-based label fusion has received an increasing interest in the medical image segmentation field. After warping the anatomical labels from the atlas images to the target image by registration, label fusion is the key step to determine the latent label for each target image point. Two popular types of patch-based label fusion approaches are (1) reconstruction-based approaches that compute the target labels as a weighted average of atlas labels, where the weights are derived by reconstructing the target image patch using the atlas image patches; and (2) classification-based approaches that determine the target label as a mapping of the target image patch, where the mapping function is often learned using the atlas image patches and their corresponding labels. Both approaches have their advantages and limitations. In this paper, we propose a novel patch-based label fusion method to combine the above two types of approaches via matrix completion (and hence, we call it transversal). As we will show, our method overcomes the individual limitations of both reconstruction-based and classification-based approaches. Since the labeling confidences may vary across the target image points, we further propose a sequential labeling framework that first labels the highly confident points and then gradually labels more challenging points in an iterative manner, guided by the label information determined in the previous iterations. We demonstrate the performance of our novel label fusion method in segmenting the hippocampus in the ADNI dataset, subcortical and limbic structures in the LONI dataset, and mid-brain structures in the SATA dataset. We achieve more accurate segmentation results than both reconstruction-based and classification-based approaches. Our label fusion method is also ranked 1st in the online SATA Multi-Atlas Segmentation Challenge. [ABSTRACT FROM AUTHOR]

Purpose: To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old. Methods: To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration. Results: To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12- month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance. Conclusions: The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods. [ABSTRACT FROM AUTHOR]

Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images. After registration a label is assigned to each target point in the target image by determining the similarity between the underlying target image patch (centered at the target point) and the aligned image patch in each atlas image. To achieve the highest level of accuracy during the label fusion process it's critical for the chosen patch similarity measurement to accurately capture the tissue/shape appearance of the anatomical structure. One major limitation of existing state-of-the-art label fusion methods is that they often apply a fixed size image patch throughout the entire label fusion procedure. Doing so may severely affect the fidelity of the patch similarity measurement, which in turn may not adequately capture complex tissue appearance patterns expressed by the anatomical structure. To address this limitation, we advance state-of-the-art by adding three new label fusion contributions: First, each image patch is now characterized by a multi-scale feature representation that encodes both local and semi-local image information. Doing so will increase the accuracy of the patch-based similarity measurement. Second, to limit the possibility of the patch-based similarity measurement being wrongly guided by the presence of multiple anatomical structures in the same image patch, each atlas image patch is further partitioned into a set of label-specific partial image patches according to the existing labels. Since image information has now been semantically divided into different patterns, these new label-specific atlas patches make the label fusion process more specific and flexible. Lastly, in order to correct target points that are mislabeled during label fusion, a hierarchical approach is used to improve the label fusion results. In particular, a coarse-to-fine iterative label fusion approach is used that gradually reduces the patch size. To evaluate the accuracy of our label fusion approach, the proposed method was used to segment the hippocampus in the ADNI dataset and 7.0 T MR images, sub-cortical regions in LONI LBPA40 dataset, mid-brain regions in SATA dataset from MICCAI 2013 segmentation challenge, and a set of key internal gray matter structures in IXI dataset. In all experiments, the segmentation results of the proposed hierarchical label fusion method with multi-scale feature representations and label-specific atlas patches are more accurate than several well-known state-of-the-art label fusion methods. [ABSTRACT FROM AUTHOR]

Despite intensive efforts for decades, deformable image registration is still a challenging problem due to the potential large anatomical differences across individual images, which limits the registration performance. Fortunately, this issue could be alleviated if a good initial deformation can be provided for the two images under registration, which are often termed as the moving subject and the fixed template, respectively. In this work, we present a novel patch-based initial deformation prediction framework for improving the performance of existing registration algorithms. Our main idea is to estimate the initial deformation between subject and template in a patch-wise fashion by using the sparse representation technique. We argue that two image patches should follow the same deformation toward the template image if their patch-wise appearance patterns are similar. To this end, our framework consists of two stages, i.e., the training stage and the application stage. In the training stage, we register all training images to the pre-selected template, such that the deformation of each training image with respect to the template is known. In the application stage, we apply the following four steps to efficiently calculate the initial deformation field for the new test subject: (1) We pick a small number of key points in the distinctive regions of the test subject; (2) for each key point, we extract a local patch and form a coupled appearance-deformation dictionary from training images where each dictionary atom consists of the image intensity patch as well as their respective local deformations; (3) a small set of training image patches in the coupled dictionary are selected to represent the image patch of each subject key point by sparse representation. Then, we can predict the initial deformation for each subject key point by propagating the pre-estimated deformations on the selected training patches with the same sparse representation coefficients; and (4) we employ thin-plate splines (TPS) to interpolate a dense initial deformation field by considering all key points as the control points. Thus, the conventional image registration problem becomes much easier in the sense that we only need to compute the remaining small deformation for completing the registration of the subject to the template. Experimental results on both simulated and real data show that the registration performance can be significantly improved after integrating our patch-based deformation prediction framework into the existing registration algorithms. [ABSTRACT FROM AUTHOR]

Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods. Hum Brain Mapp 35:5052-5070, 2014. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] A generative probability model is proposed to describe the labeling procedure. [•] The labeling dependency is explicitly modeled to achieve largest labeling unanimity among atlas patches. [•] The sparsity constraint is imposed label fusion weights in order to reduce the risk of including misleading atlas patches. [•] EM-based solution is provided to infer the labels from the generative probabilistic model. [Copyright &y& Elsevier]

Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto the standard space. Because of possible large anatomical differences across different individual brains, registration performance could be limited when trying to estimate a single directed deformation pathway, i.e., either from template to subject or from subject to template. Symmetric image registration, however, offers an effective way to simultaneously deform template and subject images toward each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the pointwise intensity matching between two images may not necessarily imply the matching of correct anatomical correspondences. Based on HAMMER registration algorithm (Shen and Davatzikos, [2002]: IEEE Trans Med Imaging 21:1421-1439), we integrate the strategies of hierarchical attribute matching and symmetric diffeomorphic deformation to build a new symmetric-diffeomorphic HAMMER registration algorithm, called as S-HAMMER. The performance of S-HAMMER has been extensively compared with 14 state-of-the-art nonrigid registration algorithms evaluated in (Klein et al., [2009]: NeuroImage 46:786-802) by using real brain images in LPBA40, IBSR18, CUMC12, and MGH10 datasets. In addition, the registration performance of S-HAMMER, by comparison with other methods, is also demonstrated on both elderly MR brain images (>70 years old) and the simulated brain images with ground-truth deformation fields. In all experiments, our proposed method achieves the best registration performance over all other registration methods, indicating the high applicability of our method in future neuroscience and clinical applications. Hum Brain Mapp 35:1044-1060, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness. [Copyright &y& Elsevier]

In many neuroscience and clinical studies, accurate measurement of hippocampus is very important to reveal the inter-subject anatomical differences or the subtle intra-subject longitudinal changes due to aging or dementia. Although many automatic segmentation methods have been developed, their performances are still challenged by the poor image contrast of hippocampus in the MR images acquired especially from 1.5 or 3.0Tesla (T) scanners. With the recent advance of imaging technology, 7.0T scanner provides much higher image contrast and resolution for hippocampus study. However, the previous methods developed for segmentation of hippocampus from 1.5T or 3.0T images do not work for the 7.0T images, due to different levels of imaging contrast and texture information. In this paper, we present a learning-based algorithm for automatic segmentation of hippocampi from 7.0T images, by taking advantages of the state-of-the-art multi-atlas framework and also the auto-context model (ACM). Specifically, ACM is performed in each atlas domain to iteratively construct sequences of location-adaptive classifiers by integrating both image appearance and local context features. Due to the plenty texture information in 7.0T images, more advanced texture features are also extracted and incorporated into the ACM during the training stage. Then, under the multi-atlas segmentation framework, multiple sequences of ACM-based classifiers are trained for all atlases to incorporate the anatomical variability. In the application stage, for a new image, its hippocampus segmentation can be achieved by fusing the labeling results from all atlases, each of which is obtained by applying the atlas-specific ACM-based classifiers. Experimental results on twenty 7.0T images with the voxel size of 0.35×0.35×0.35mm3 show very promising hippocampus segmentations (in terms of Dice overlap ratio 89.1±0.020), indicating high applicability for the future clinical and neuroscience studies. [ABSTRACT FROM AUTHOR]

Abstract: In discrete element model (DEM) for simulation of gas–solid fluidized bed, drag force is correlated by use of local parameters which would be excessively smoothed within coarse grid. This work studies the method of calculating drag force for coarse grid DEM simulation of bubbling fluidized bed. The so-called complete circumstance-dependent local porosity (CCLP) model is proposed to correlate drag force taking the local circumstance of particle into account. The resulting circumstance-dependent drag force is employed in two-dimensional DEM simulations of a real small-scale bubbling fluidized system, using coarse grid. The local porosity is shown to be revised to a greater extent at the junctions of the gas-rich and particle-rich regions by the proposed CCLP model, which indicates that the circumstance influences on drag force are highlighted in the dense–dilute transitional regions. The parameter, i.e. the amplification factor, involved in the CCLP model is rationalized by investigating the comprehensive capability of predicting the fluidization features. The bubble size and shape simulated at the optimal parameter are generally consistent with the experimental observations. The simulated fluctuation time scales and amplitudes of solid volume fraction, relative pressure and bed layer height are close to the experimental results. Simulations show that DEM performs well in modeling the time-varying waveforms for the physical quantities in bubbling fluidized bed by use of the revised drag calculation method, which proves the unfamiliar merits of coarse grid simulation in some ways. [Copyright &y& Elsevier]

Robust anatomical correspondence detection is a key step in many medical image applications such as image registration and motion correction. In the computer vision field, graph matching techniques have emerged as a powerful approach for correspondence detection. By considering potential correspondences as graph nodes, graph edges can be used to measure the pairwise agreement between possible correspondences. In this paper, we present a novel, hierarchical graph matching method with sparsity constraint to further augment the power of conventional graph matching methods in establishing anatomical correspondences, especially for the cases of large inter-subject variations in medical applications. Specifically, we first propose to measure the pairwise agreement between potential correspondences along a sequence of intensity profiles which reduces the ambiguity in correspondence matching. We next introduce the concept of sparsity on the fuzziness of correspondences to suppress the distraction from misleading matches, which is very important for achieving the accurate, one-to-one correspondences. Finally, we integrate our graph matching method into a hierarchical correspondence matching framework, where we use multiple models to deal with the large inter-subject anatomical variations and gradually refine the correspondence matching results between the tentatively deformed model images and the underlying subject image. Evaluations on both synthetic data and public hand X-ray images indicate that the proposed hierarchical sparse graph matching method yields the best correspondence matching performance in terms of both accuracy and robustness when compared with several conventional graph matching methods. [ABSTRACT FROM PUBLISHER]

Abstract: In discrete element method (DEM) for simulations of gas–solid fluidized beds, coarse grid calculation can lead to excessive smoothing of local porosities for correlating drag on individual particles. This article tries to calculate local porosities with consideration of the heterogeneous particle distribution within a coarse grid. The compromise-based local porosity (CLP) model is set up and incorporated into coarse grid DEM simulations of gas–solid fluidized beds with large particles. Simulated bubble size and shape are in good agreement with experimental results. Various complex bubbling phenomena are described such as bubble eruption, bubble rupture and bubble coalescence. The fluctuations of solid volume fraction, relative pressure and bed layer height simulated at the fine-tuned parameters are in general agreement with the experimental data. These results indicate that the proposed CLP model is suitable to characterize distinct heterogeneities within coarse grids. [Copyright &y& Elsevier]

Purpose: One of the major challenges of lung cancer radiation therapy is how to reduce the margin of treatment field but also manage geometric uncertainty from respiratory motion. To this end, 4D-CT imaging has been widely used for treatment planning by providing a full range of respiratory motion for both tumor and normal structures. However, due to the considerable radiation dose and the limit of resource and time, typically only a free-breathing 3D-CT image is acquired on the treatment day for image-guided patient setup, which is often determined by the image fusion of the free-breathing treatment and planning day 3D-CT images. Since individual slices of two free breathing 3D-CTs are possibly acquired at different phases, two 3D-CTs often look different, which makes the image registration very challenging. This uncertainty of pretreatment patient setup requires a generous margin of radiation field in order to cover the tumor sufficiently during the treatment. In order to solve this problem, our main idea is to reconstruct the 4D-CT (with full range of tumor motion) from a single free-breathing 3D-CT acquired on the treatment day. Methods: We first build a super-resolution 4D-CT model from a low-resolution 4D-CT on the planning day, with the temporal correspondences also established across respiratory phases. Next, we propose a 4D-to-3D image registration method to warp the 4D-CT model to the treatment day 3D-CT while also accommodating the new motion detected on the treatment day 3D-CT. In this way, we can more precisely localize the moving tumor on the treatment day. Specifically, since the free-breathing 3D-CT is actually the mixed-phase image where different slices are often acquired at different respiratory phases, we first determine the optimal phase for each local image patch in the free-breathing 3D-CT to obtain a sequence of partial 3D-CT images (with incomplete image data at each phase) for the treatment day. Then we reconstruct a new 4D-CT for the treatment day by registering the 4D-CT of the planning day (with complete information) to the sequence of partial 3D-CT images of the treatment day, under the guidance of the 4D-CT model built on the planning day. Results: We first evaluated the accuracy of our 4D-CT model on a set of lung 4D-CT images with manually labeled landmarks, where the maximum error in respiratory motion estimation can be reduced from 6.08 mm by diffeomorphic Demons to 3.67 mm by our method. Next, we evaluated our proposed 4D-CT reconstruction algorithm on both simulated and real free-breathing images. The reconstructed 4D-CT using our algorithm shows clinically acceptable accuracy and could be used to guide a more accurate patient setup than the conventional method. Conclusions: We have proposed a novel two-step method to reconstruct a new 4D-CT from a single free-breathing 3D-CT on the treatment day. Promising reconstruction results imply the possible application of this new algorithm in the image guided radiation therapy of lung cancer. [ABSTRACT FROM AUTHOR]

Four-dimensional computed tomography (4D-CT) plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior–inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior–inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior–inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future. [ABSTRACT FROM AUTHOR]

In this paper, we propose a general framework for performance improvement of the current state-of-the-art registration algorithms in terms of both accuracy and computation time. The key concept involves rapid prediction of a deformation field for registration initialization, which is achieved by a statistical correlation model learned between image appearances and deformation fields. This allows us to immediately bring a template image as close as possible to a subject image that we need to register. The task of the registration algorithm is hence reduced to estimating small deformation between the subject image and the initially warped template image, i.e., the intermediate template (IT). Specifically, to obtain a good subject-specific initial deformation, support vector regression is utilized to determine the correlation between image appearances and their respective deformation fields. When registering a new subject onto the template, an initial deformation field is first predicted based on the subject's image appearance for generating an IT. With the IT, only the residual deformation needs to be estimated, presenting much less challenge to the existing registration algorithms. Our learning-based framework affords two important advantages: 1) by requiring only the estimation of the residual deformation between the IT and the subject image, the computation time can be greatly reduced; 2) by leveraging good deformation initialization, local minima giving suboptimal solution could be avoided. Our framework has been extensively evaluated using medical images from different sources, and the results indicate that, on top of accuracy improvement, significant registration speedup can be achieved, as compared with the case where no prediction of initial deformation is performed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel image registration method is proposed to achieve accurate registration between images having large shape differences with the help of a set of appropriate intermediate templates. We first demonstrate that directionality is a key factor in both pairwise image registration and groupwise registration, which is defined in this paper to describe the influence of the registration direction and paths on the registration performance. In our solution, the intermediate template selection and intermediate template guided registration are two coherent steps with directionality being considered. To take advantage of the directionality, a directed graph is built based on the asymmetric distance defined on all ordered image pairs in the image population, which is fundamentally different from the undirected graph with symmetric distance metrics in all previous methods, and the shortest distance between template and subject on the directed graph is calculated. The allocated directed path can be thus utilized to better guide the registration by successively registering the subject through the intermediate templates one by one on the path towards the template. The proposed directed graph based solution can also be used in groupwise registration. Specifically, by building a minimum spanning arborescence (MSA) on the directed graph, the population center, i.e., a selected template, as well as the directed registration paths from all the rest of images to the population center, is determined simultaneously. The performance of directed graph based registration algorithm is demonstrated by the spatial normalization on both synthetic dataset and real brain MR images. It is shown that our method can achieve more accurate registration results than both the undirected graph based solution and the direct pairwise registration. [Copyright &y& Elsevier]

Groupwise registration has been widely investigated in recent years due to its importance in analyzing population data in many clinical applications. To our best knowledge, most of the groupwise registration algorithms only utilize the intensity information. However, it is well known that using intensity only is not sufficient to achieve the anatomically sound correspondences in medical image registration. In this article, we propose a novel feature-based groupwise registration algorithm to establish the anatomical correspondence across subjects by using the attribute vector that is defined as the morphological signature for each voxel. Similar to most of the state-of-the-art groupwise registration algorithms, which simultaneously estimate the transformation fields for all subjects, we develop an energy function to minimize the intersubject discrepancies on anatomical structures and drive all subjects toward the hidden common space. To make the algorithm efficient and robust, we decouple the complex groupwise registration problem into two easy-to-solve subproblems, namely (1) robust correspondence detection and (2) dense transformation field estimation, which are systematically integrated into a unified framework. To achieve the robust correspondences in the step (1), several strategies are adopted. First, the procedure of feature matching is evaluated within a neighborhood, rather than only on a single voxel. Second, the driving voxels with distinctive image features are designed to drive the transformations of other nondriving voxels. Third, we take advantage of soft correspondence assignment not only in the spatial domain but also across the population of subjects. Specifically, multiple correspondences are allowed to alleviate the ambiguity in establishing correspondences w.r.t. a particular subject and also the contributions from different subjects are dynamically controlled throughout the registration. Eventually in the step (2), based on the correspondences established for the driving voxels, thin-plate spline is used to propagate correspondences on the driving voxels to other locations in the image. By iteratively repeating correspondence detection and dense deformation estimation, all the subjects will be aligned onto the common space. Our feature-based groupwise registration algorithm has been extensively evaluated over 18 elderly brains, 16 brains from NIREP (with 32 manually delineated labels), 40 brains from LONI LPBA40 (with 54 manually delineated labels), and 12 pairs of normal controls and simulated atrophic brain images. In all experiments, our algorithm achieves more robust and accurate registration results, compared with another groupwise algorithm and a pairwise registration method. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Accurate measurement of longitudinal changes of brain structures and functions is very important but challenging in many clinical studies. Also, across-subject comparison of longitudinal changes is critical in identifying disease-related changes. In this paper, we propose a novel method to meet these two requirements by simultaneously registering sets of longitudinal image sequences of different subjects to the common space, without assuming any explicit template. Specifically, our goal is to 1) consistently measure the longitudinal changes from a longitudinal image sequence of each subject, and 2) jointly align all image sequences of different subjects to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial–temporal behavior of anatomical changes in each longitudinal image sequence. Then, a probabilistic model is built upon the temporal fibers to characterize both spatial smoothness and temporal continuity. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of the probabilistic models. Promising results have been obtained in quantitative measurement of longitudinal brain changes, i.e., hippocampus volume changes, showing better performance than those obtained by either the pairwise or the groupwise only registration methods. [Copyright &y& Elsevier]

Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. Methods: First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Results: Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.Conclusions: The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets. [ABSTRACT FROM AUTHOR]

Abstract: Groupwise registration has become more and more popular due to its attractiveness for unbiased analysis of population data. One of the most popular approaches for groupwise registration is to iteratively calculate the group mean image and then register all subject images towards the latest estimated group mean image. However, its performance might be undermined by the fuzzy mean image estimated in the very beginning of groupwise registration procedure, because all subject images are far from being well-aligned at that moment. In this paper, we first point out the significance of always keeping the group mean image sharp and clear throughout the entire groupwise registration procedure, which is intuitively important but has not been explored in the literature yet. To achieve this, we resort to developing the robust mean-image estimator by the adaptive weighting strategy, where the weights are adaptive across not only the individual subject images but also all spatial locations in the image domain. On the other hand, we notice that some subjects might have large anatomical variations from the group mean image, which challenges most of the state-of-the-art registration algorithms. To ensure good registration results in each iteration, we explore the manifold of subject images and build a minimal spanning tree (MST) with the group mean image as the root of the MST. Therefore, each subject image is only registered to its parent node often with similar shapes, and its overall transformation to the group mean image space is obtained by concatenating all deformations along the paths connecting itself to the root of the MST (the group mean image). As a result, all the subjects will be well aligned to the group mean image adaptively. Our method has been evaluated in both real and simulated datasets. In all experiments, our method outperforms the conventional algorithm which generally produces a fuzzy group mean image throughout the entire groupwise registration. [Copyright &y& Elsevier]

Abstract: To achieve more accurate and consistent registration in an image population, a novel hierarchical groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this new framework, the global structure, i.e., the relative distribution of subject images is always preserved during the registration process by constraining each subject image to deform only locally with respect to its neighbors within the learned image manifold. To achieve this goal, two novel strategies, i.e., the self-organized registration by warping one image towards a set of its eligible neighbors and image bundling to cluster similar images, are specially proposed. By using these two strategies, this new framework can perform groupwise registration in a hierarchical way. Specifically, in the high level, it will perform on a much smaller dataset formed by the representative subject images of all subgroups that are generated in the previous levels of registration. Compared to the other groupwise registration methods, our proposed framework has several advantages: (1) it explores the local data distribution and uses the obtained distribution information to guide the registration; (2) the possible registration error can be greatly reduced by requiring each individual subject to move only towards its nearby subjects with similar structures; (3) it can produce a smoother registration path, in general, from each subject image to the final built atlas than other groupwise registration methods. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise registration methods, in terms of both registration accuracy and robustness. [Copyright &y& Elsevier]

Abstract: Groupwise registration has been recently introduced to simultaneously register a group of images by avoiding the selection of a particular template. To achieve this, several methods have been proposed to take advantage of information-theoretic entropy measures based on image intensity. However, simplistic utilization of voxelwise image intensity is not sufficient to establish reliable correspondences, since it lacks important contextual information. Therefore, we explore the notion of attribute vector as the voxel signature, instead of image intensity, to guide the correspondence detection in groupwise registration. In particular, for each voxel, the attribute vector is computed from its multi-scale neighborhoods, in order to capture the geometric information at different scales. The probability density function (PDF) of each element in the attribute vector is then estimated from the local neighborhood, providing a statistical summary of the underlying anatomical structure in that local pattern. Eventually, with the help of Jensen–Shannon (JS) divergence, a group of subjects can be aligned simultaneously by minimizing the sum of JS divergences across the image domain and all attributes. We have employed our groupwise registration algorithm on both real (NIREP NA0 data set) and simulated data (12 pairs of normal control and simulated atrophic data set). The experimental results demonstrate that our method yields better registration accuracy, compared with a popular groupwise registration method. [Copyright &y& Elsevier]

Abstract: In this paper, by solving the relaxed quasiconcave programming problem in outcome space, a new global optimization algorithm for convex multiplicative programming is presented. Two kinds of techniques are employed to establish the algorithm. The first one is outer approximation technique which is applied to shrink relaxation area of quasiconcave programming problem and to compute appropriate feasible points and to raise the capacity of bounding. And the other one is branch and bound technique which is used to guarantee global optimization. Some numerical results are presented to demonstrate the effectiveness and feasibility of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: We present an improved MR brain image registration algorithm, called TPS-HAMMER, which is based on the concepts of attribute vectors and hierarchical landmark selection scheme proposed in the highly successful HAMMER registration algorithm. We demonstrate that TPS-HAMMER algorithm yields better registration accuracy, robustness, and speed over HAMMER owing to (1) the employment of soft correspondence matching and (2) the utilization of thin-plate splines (TPS) for sparse-to-dense deformation field generation. These two aspects can be integrated into a unified framework to refine the registration iteratively by alternating between soft correspondence matching and dense deformation field estimation. Compared with HAMMER, TPS-HAMMER affords several advantages: (1) unlike the Gaussian propagation mechanism employed in HAMMER, which can be slow and often leaves unreached blotches in the deformation field, the deformation interpolation in the non-landmark points can be obtained immediately with TPS in our algorithm; (2) the smoothness of deformation field is preserved due to the nice properties of TPS; (3) possible misalignments can be alleviated by allowing the matching of the landmarks with a number of possible candidate points and enforcing more exact matches in the final stages of the registration. Extensive experiments have been conducted, using the original HAMMER as a comparison baseline, to validate the merits of TPS-HAMMER. The results show that TPS-HAMMER yields significant improvement in both accuracy and speed, indicating high applicability for the clinical scenario. [Copyright &y& Elsevier]

Abstract: We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods (Yang et al., 2008; Zhang et al., 2006). [Copyright &y& Elsevier]

A new crossed molecular beam apparatus has been constructed for investigating polyatomic chemical reactions using the time-sliced ion velocity map imaging technique. A unique design is adopted for one of the two beam sources and allows us to set up the molecular beam source either horizontally or vertically. This can be conveniently used to produce versatile atomic or radical beams from photodissociation and as well as electric discharge. Intensive H-atom beam source with high speed ratio was produced by photodissociation of the HI molecule and was reacted with the CD4 molecule. Vibrational-state resolved HD product distribution was measured by detecting the CD3 product. Preliminary results were also reported on the F+SiH4 reaction using the discharged F atom beam. These results demonstrate that this new instrument is a powerful tool for investigating chemical dynamics of polyatomic reactions. [ABSTRACT FROM AUTHOR]

The vibrationally excited reaction O(1D) + CHD3(ν1 = 1) has been investigated by crossed-molecular-beam experiments with a time-sliced velocity map imaging technique. Detailed and quantitative information is extracted on the C–H stretching excitation effects on the reactivity and dynamics of the title reaction, with the help of preparation of C–H stretching excited CHD3 molecules by direct infrared excitation. Experimental results show that the vibrational stretching excitation of the C–H bond almost does not affect the relative contributions between different dynamical pathways for all product channels. For the OH + CD3 product channel, the vibrational energy of the C–H stretching excited CHD3 reagent is channeled exclusively into the vibrational energy of the OH products. The vibrational excitation of the CHD3 reactant changes the reactivities for the ground-state and umbrella-mode-excited CD3 channels very modestly, while it significantly suppresses the corresponding CHD2 channels. For the CHD2(ν1 = 1) channel, the stretching excited C–H bond of the CHD3 molecule acts almost as a pure spectator. [ABSTRACT FROM AUTHOR]

• The complexation of non-Hantcsh type 1,4-dihydropyridine with Cu(II) was investigated for the first time. • An unexpected Cu(II) complex of oxidized 1,4-dydropyridine was obtained and detailed structural studies were performed experimentally and theoretically. • Oxidation mechanism of 1,4-dydropyridine was revealed by DFT calculations for the first time. A Cu(II) complex of 4-phenyl-3,5-dicarboxylic-pyridine (HL 2), [ Cu(HL 2)Py ], was isolated unexpectedly during studying coordination reaction of 3,5-dicarboxylic-1,4-dihydropyridine-4-phenyl ligand (HL 1) with copper(II) nitrate by solvothermal method. Crystal structure of the [ Cu(HL 2)Py ] was determined by single-crystal X-Ray diffraction and its phase purity and thermal stability was evaluated by PXRD and TGA, respectively. Furthermore, to shed more light on structural properties of the [ Cu(HL 2)Py ], its geometrical and electronic structure was investigated experimentally and theoretically. Finally, the oxidative mechanism of HL 1 was investigated by DFT calculations. [ABSTRACT FROM AUTHOR]

Embedded machine vision algorithm development platform is of great significance. Based on the elaboration of visual communication design, this paper further details the design of fractal visual art graphics based on computer-aided algorithms to design edge detection schemes, where edges represent sudden changes in the signal and are able to characterize the rich information of an image. In response to the problems of poor robustness and difficult parameter selection of the currently widely used edge detection algorithms, the platform encapsulates joint edge detection graphic components in a higher-order processing library to achieve the requirements of high-precision detection and to realise enhanced edge detection effects. Finally, the experimental analysis shows that the proposed algorithm has a more accurate detection effect and can meet the user's high-precision detection requirements. [ABSTRACT FROM AUTHOR]

Extracting uranium from strongly acidic uranium-containing wastewater by a gentle and efficient method is a globally challenge problem. In this paper, sulfonated-polyethersulfone (SPES) membranes and [NH 4 +[COF–SO 3 -/SPES mixed matrix membranes (MMMs) were synthesized and for the first time investigated for the extraction of uranium from acidic uranium aqueous solutions of pH ​= ​1, filling the void of SPES-based membranes in the application in uranium absorption. The static adsorption thermodynamics of SPES membranes and [NH 4 +[COF–SO 3 -/SPES MMMs towards uranium were investigated, and the adsorption mechanism was studied through the measurement of SEM, FT-IR, XPS and EDX. The results displayed that, SPES membranes demonstrated outstanding adsorption capacity of 42.1 ​mg g−1and [NH 4 +[COF–SO 3 -/SPES MMMs showed the record-breaking adsorption capacity of 99.4 ​mg ​g−1 at pH ​= ​1, resulting from the synergistic adsorption effect of [NH 4 +[COF–SO 3 - and SPES. Moreover, the kinetics, dynamic adsorption/desorption and membrane reusability of [NH 4 +[COF–SO 3 -/SPES MMMs were investigated. It was found that, the [NH 4 +[COF–SO 3 -/SPES MMMs exhibited high removal rate of 99%, and excellent reusability even after five recycles, demonstrating great potential in the recovery of uranium from uranium mine water. SPES membranes and [NH 4 +[COF–SO 3 -/SPES mixed matrix membranes (MMMs) were synthesized and for the first time applied for the extraction of uranium from acidic uranium aqueous solutions of pH ​= ​1. The SPES membranes demonstrated outstanding adsorption capacity of 42.1 ​mg g−1and [NH 4 +[COF–SO 3 -/SPES MMMs showed the record-breaking adsorption capacity of 99.4 ​mg ​g−1 at pH ​= ​1, displaying excellent great potential in extracting uranium from uranium-containing wastewater. Image 1 [ABSTRACT FROM AUTHOR]

Structural characterization of neutral water clusters is crucial to understanding the structures and properties of water, but it has been proven to be a challenging experimental target due to the difficulty in size selection. Here, we report the size-specific infrared spectra of confinement-free neutral water nonamer (H2O)9 based on threshold photoionization, using a tunable vacuum ultraviolet free-electron laser. Distinct OH stretch vibrational fundamentals in the 3200–3350 cm−1 region are observed, providing unique spectral signatures for the formation of an unprecedented (H2O)9 structure evolved by adding a ninth water molecule onto a hydrogen bond-unbroken edge of the (H2O)8 octamer with D2d symmetry. This nonamer structure coexists with the five previously identified structures that can be viewed as derived by inserting a ninth water molecule into a hydrogen bond-broken edge of the D2d/S4 octamer. These findings provide key microscopic information for systematic understanding of the formation and growth mechanism of dynamical hydrogen-bonding networks that are responsible for the structure and properties of condensed-phase water. [ABSTRACT FROM AUTHOR]

Single‐component white‐light emitters are ideal for solid‐state lighting applications. Here, the dual‐emission white light in the 3D Cs2NaInCl6:0.1 Sb3+ crystal is successfully achieved by the high‐pressure treatment strategy. Upon compression, the remarkable self‐trapped exciton (STE) emission enhancement is observed at 20.6 GPa due to the tilting and twisting of inorganic octahedron caused by the phase transition of Cs2NaInCl6:0.1 Sb3+ crystal. Intriguingly, after undergoing the high‐pressure treatment, in addition to the blue high‐energy emission from the STEs, [SbCl6]3− exhibits the orange low‐energy emission from STEs, which is ascribed to the remaining local distortion of inorganic octahedron resulting from the presence of residual amorphous phase in the local region upon decompression. Furthermore, coherent phonon generation and density functional theory calculations indicate that the electrons exhibit a strong coupling to A1g stretch mode and T2gIn bending mode in Cs2NaInCl6:0.1 Sb3+. After high‐pressure treatment, the coupling between the electrons and the T2gIn bending mode disappears, suggesting local octahedral symmetry disruption due to residual amorphous structures induced by high‐pressure treatment. This work unveils the relationship between the electron‐phonon coupling and the STE emission and offers a new strategy to design and synthesize the new single‐component white‐light metal halide perovskites. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta‐amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill‐posed results that are sometimes inconsistent with the essential neurological principles. Purpose: Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. Methods: We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction‐diffusion model, where we consider non‐linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long‐term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Results: Our stratification achieves superior performance in both inter‐cluster heterogeneity and intra‐cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. Conclusions: The proposed PSSN (i) reduces neuroimage data to low‐dimensional feature vectors, (ii) combines AT[N]‐Net based on real pathological pathways, (iii) predicts long‐term biomarker trajectories, and (iv) stratifies subjects into fine‐grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre‐symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]