arXiv subjects

Qian Tao

Publications and source records attributed to Qian Tao.

At least 55 records · Page 3Linked to original sources

arXiv2024

Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction

Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as the regularization term. In quantitative MRI, the physical model of nuclear magnetic resonance relaxometry is known, providing additional prior knowledge for image reconstruction. However, traditional reconstruction networks are limited to learning the spatial domain prior knowledge, ignoring the relaxometry prior. Therefore, we propose a relaxometry-guided quantitative MRI reconstruction framework to learn the spatial prior from data and the relaxometry prior from MRI physics. Additionally, we also evaluated the performance of two popular reconstruction backbones, namely, recurrent variational networks (RVN) and variational networks (VN) with U- Net. Experiments demonstrate that the proposed method achieves highly promising results in quantitative MRI reconstruction.

eess.IV↗

arXiv2024

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI. CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.

eess.IV↗

arXiv2024

Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields

Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreatment, an accurate prediction of tumor growth based on longitudinal imaging is highly desirable. In this paper, we introduce DeepGrowth, a deep learning method that incorporates neural fields and recurrent neural networks for prospective tumor growth prediction. In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code. Unlike previous studies that perform tumor shape prediction directly in the image space, we predict the latent codes instead and then reconstruct future shapes from it. To deal with irregular time intervals, we introduce a time-conditioned recurrent module based on a ConvLSTM and a novel temporal encoding strategy, which enables the proposed model to output varying tumor shapes over time. The experiments on an in-house longitudinal VS dataset showed that the proposed model significantly improved the performance ($\ge 1.6\%$ Dice score and $\ge0.20$ mm 95\% Hausdorff distance), in particular for top 20\% tumors that grow or shrink the most ($\ge 4.6\%$ Dice score and $\ge 0.73$ mm 95\% Hausdorff distance). Our code is available at ~\burl{https://github.com/cyjdswx/DeepGrowth}

eess.IV↗

arXiv2024

CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation

Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients, limiting the utilization of deep learning models trained on multi-sequence data. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition. State-of-the-art methods tackling this problem are based on convolutional neural networks (CNN) which usually suffer from spectral biases, resulting in poor reconstruction of high-frequency fine details. In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation. The proposed model uses a multi-layer perceptron (MLP) instead of a CNN as the decoder for pixel-to-pixel mapping. Hence, each target image is represented as a neural field that is conditioned on the source image via shift modulation with a learned latent code. Experiments on BraTS 2018 and an in-house clinical dataset of vestibular schwannoma patients showed that the proposed method outperformed state-of-the-art methods for multi-sequence MRI translation both visually and quantitatively. Moreover, we conducted spectral analysis, showing that CoNeS was able to overcome the spectral bias issue common in conventional CNN models. To further evaluate the usage of synthesized images in clinical downstream tasks, we tested a segmentation network using the synthesized images at inference.

eess.IV↗

arXiv2024

Superconductivity emerging from density-wave-like order in a correlated kagome metal

Unconventional superconductivity (USC) in a highly correlated kagome system has been theoretically proposed for years, yet the experimental realization is hard to achieve. The recently discovered vanadium-based kagome materials, which exhibit both superconductivity and charge density wave (CDW) orders, are nonmagnetic and weakly correlated, thus unlikely host USC as theories proposed. Here we report the discovery of a chromium-based kagome metal, CsCr$_3$Sb$_5$, which is contrastingly characterised by strong electron correlations, frustrated magnetism, and characteristic flat bands close to the Fermi level. Under ambient pressure, it undergoes a concurrent structural and magnetic phase transition at 55 K, accompanying with a stripe-like $4a_0$ structural modulation. At high pressure, the phase transition evolves into two transitions, probably associated with CDW and antiferromagnetic spin-density-wave orderings, respectively. These density-wave (DW)-like orders are gradually suppressed with pressure and, remarkably, a superconducting dome emerges at 3.65-8.0 GPa. The maximum of the superconducting transition temperature, $T_\mathrm{c}^{\mathrm{max}}=$ 6.4 K, appears when the DW-like orders are completely suppressed at 4.2 GPa, and the normal state exhibits a non-Fermi-liquid behaviour, reminiscent of USC and quantum criticality in iron-based superconductors. Our work offers an unprecedented platform for investigating possible USC in a correlated kagome system.

cond-mat.supr-con↗

arXiv2023

Third order nonlinear transport properties in topological chiral antiferromagnetic semimetal CoNb3S6

The topology between Bloch states in reciprocal space has attracted tremendous attention in recent years. The quantum geometry of the band structure is composed of quantum metric as real part and berry curvature as imaginary part. While the Berry curvature, the Berry curvature dipole and Berry connection polarizability have been recently revealed by the first order anomalous hall, second order and third order nonlinear Hall effect respectively, the quantum metric induced second order nonlinear transverse and longitudinal response in topological antiferromagnetic material MnBi2Te4 was only very recently reported. Here we demonstrate the similar third order nonlinear transport properties in the topological antiferromagnetic CoNb3S6. We observed that the third order nonlinear longitudinal V3ω xx increase significantly at the antiferromagnetic transition temperature TN ~ 29 K, which was probably induced by the quantum metric without time-reversal symmetry or inversion symmetry. Besides, temperature-dependent nonlinear behaviour was observed in the first order I-V curve below the Neel temperature TN, which was not reported in MnBi2Te4 and FeSn. Such nonlinear I-V behaviour hints for the possible existence of Charge Density Wave (CDW) state, which has been discovered in its sister material FeNb3S6. Simultaneously, two plateaus in the third order nonlinear longitudinal V3ω xx~ I^ω curve are observed, which is also speculated to be related with the possible CDW state. However, the genuine mechanism for the first order nonlinear I-V and its relation with the third order nonlinear transport call for more experimental investigations and theoretical interpretation. Our work provides a way to explore third harmonic nonlinear transport and interaction with magnetic order and CDW.

cond-mat.mtrl-sci↗

arXiv2023

Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI

Quantitative cardiac magnetic resonance imaging (MRI) is an increasingly important diagnostic tool for cardiovascular diseases. Yet, co-registration of all baseline images within the quantitative MRI sequence is essential for the accuracy and precision of quantitative maps. However, co-registering all baseline images from a quantitative cardiac MRI sequence remains a nontrivial task because of the simultaneous changes in intensity and contrast, in combination with cardiac and respiratory motion. To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework. The low-rank component of rPCA corresponds to the quantitative mapping (i.e. limited degree of freedom in variation), while the sparse component corresponds to the residual motion, making it easier to formulate and solve the groupwise registration problem. We evaluated our proposed method on cardiac T1 mapping by the modified Look-Locker inversion recovery (MOLLI) sequence, both before and after the Gadolinium contrast agent administration. Our experiments showed that our method effectively improved registration performance over baseline methods without introducing rPCA, and reduced quantitative mapping error in both in-domain (pre-contrast MOLLI) and out-of-domain (post-contrast MOLLI) inference. The proposed rPCA framework is generic and can be integrated with other registration backbones.

eess.IV↗

arXiv2023

Local Implicit Neural Representations for Multi-Sequence MRI Translation

In radiological practice, multi-sequence MRI is routinely acquired to characterize anatomy and tissue. However, due to the heterogeneity of imaging protocols and contra-indications to contrast agents, some MRI sequences, e.g. contrast-enhanced T1-weighted image (T1ce), may not be acquired. This creates difficulties for large-scale clinical studies for which heterogeneous datasets are aggregated. Modern deep learning techniques have demonstrated the capability of synthesizing missing sequences from existing sequences, through learning from an extensive multi-sequence MRI dataset. In this paper, we propose a novel MR image translation solution based on local implicit neural representations. We split the available MRI sequences into local patches and assign to each patch a local multi-layer perceptron (MLP) that represents a patch in the T1ce. The parameters of these local MLPs are generated by a hypernetwork based on image features. Experimental results and ablation studies on the BraTS challenge dataset showed that the local MLPs are critical for recovering fine image and tumor details, as they allow for local specialization that is highly important for accurate image translation. Compared to a classical pix2pix model, the proposed method demonstrated visual improvement and significantly improved quantitative scores (MSE 0.86 x 10^-3 vs. 1.02 x 10^-3 and SSIM 94.9 vs 94.3)

eess.IV↗

arXiv2023

DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness

Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original intrinsic or artificially established graph structure which may not accurately reflect the "true" correlation among data and are not optimal for semi-supervised node classification in the downstream graph neural networks. Besides, while existing graph-based methods mostly utilize the explicit graph structure, some implicit information, for example, the density information, can also provide latent information that can be further exploited. To address these limitations, this paper proposes the Dual Hypergraph Neural Network (DualHGNN), a new dual connection model integrating both hypergraph structure learning and hypergraph representation learning simultaneously in a unified architecture. The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization. Then, DualHGNN employs a density-aware hypergraph attention network to explore the high-order semantic correlation among data points based on the density-aware attention mechanism. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.

cs.LG↗

arXiv2023

Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To address this problem, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph, which is much smaller than the original graph, and the lower layer has some disjointed subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. In this way, many vertices and edges are not involved in iterative computations, significantly reducing the communication overhead and improving incremental graph processing performance. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08X on average (up to 36.66X) in response time.

cs.DC↗

arXiv2023

LON-GNN: Spectral GNNs with Learnable Orthonormal Basis

In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks. Although various kinds of polynomial bases have been explored, each such method adopts a fixed polynomial basis which might not be the optimal choice for the given graph. Besides, we identify the so-called over-passing issue of these methods and show that it is somewhat rooted in their less-principled regularization strategy and unnormalized basis. In this paper, we make the first attempts to address these two issues. Leveraging Jacobi polynomials, we design a novel spectral GNN, LON-GNN, with Learnable OrthoNormal bases and prove that regularizing coefficients becomes equivalent to regularizing the norm of learned filter function now. We conduct extensive experiments on diverse graph datasets to evaluate the fitting and generalization capability of LON-GNN, where the results imply its superiority.

cs.LG↗

arXiv2023

Giant Nernst effect in the crossover between Fermi liquid and strange metal

The strange-metal state is a crucial problem in condensed matter physics highlighted by its ubiquity in almost all major correlated systems[1-7]. Its understanding could provide important insight into high-Tc superconductivity[2] and quantum criticality[8]. However, with the Fermi liquid theory failing in strange metals, understanding the highly unconventional behaviors has been a long-standing challenge. Fundamental aspects of strange metals remain elusive, including the nature of their charge carriers[1]. Here, we report the observation of a giant Nernst response in the strange-metal state in a two-dimensional superconductor 2M-WS2. A giant Nernst coefficient comparable to the vortex Nernst signal in superconducting cuprates, and its high sensitivity to carrier mobility, are found when the system enters the strange-metal state from the Fermi liquid state. The temperature and magnetic field dependence of the giant Nernst peak rule out the relevance of both Landau quasiparticles and superconductivity. Instead, the giant Nernst peak at the crossover indicates a dramatic change in carrier entropy when entering the strange-metal state. The presence of such an anomalous Nernst response is further confirmed in other iconic strange metals, suggesting its universality and places stringent experimental constraints on the mechanism of strange metals.

cond-mat.str-el↗

arXiv2022

Deep Recursive Embedding for High-Dimensional Data

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this paper, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods.

cs.LG↗

arXiv2022

Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification

Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications. One of the most challenging problems for graph-based semi-supervised node classification is how to use the implicit information among various data to improve the performance of classifying. Traditional studies on graph-based semi-supervised learning have focused on the pairwise connections among data. However, the data correlation in real applications could be beyond pairwise and more complicated. The density information has been demonstrated to be an important clue, but it is rarely explored in depth among existing graph-based semi-supervised node classification methods. To develop a flexible and effective model for graph-based semi-supervised node classification, we propose a novel Density-Aware Hyper-Graph Neural Networks (DA-HGNN). In our proposed approach, hyper-graph is provided to explore the high-order semantic correlation among data, and a density-aware hyper-graph attention network is presented to explore the high-order connection relationship. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.

cs.LG↗

arXiv2022

Deep Recursive Embedding for High-Dimensional Data

t-distributed stochastic neighbor embedding (t-SNE) is a well-established visualization method for complex high-dimensional data. However, the original t-SNE method is nonparametric, stochastic, and often cannot well prevserve the global structure of data as it emphasizes local neighborhood. With t-SNE as a reference, we propose to combine the deep neural network (DNN) with the mathematical-grounded embedding rules for high-dimensional data embedding. We first introduce a deep embedding network (DEN) framework, which can learn a parametric mapping from high-dimensional space to low-dimensional embedding. DEN has a flexible architecture that can accommodate different input data (vector, image, or tensor) and loss functions. To improve the embedding performance, a recursive training strategy is proposed to make use of the latent representations extracted by DEN. Finally, we propose a two-stage loss function combining the advantages of two popular embedding methods, namely, t-SNE and uniform manifold approximation and projection (UMAP), for optimal visualization effect. We name the proposed method Deep Recursive Embedding (DRE), which optimizes DEN with a recursive training strategy and two-stage losse. Our experiments demonstrated the excellent performance of the proposed DRE method on high-dimensional data embedding, across a variety of public databases. Remarkably, our comparative results suggested that our proposed DRE could lead to improved global structure preservation.

cs.CV↗

arXiv2021

Coexistence of superconductivity and antiferromagentic order in Er$_{2}$O$_{2}$Bi with anti-ThCr$_{2}$Si$_{2}$ structure

We investigated the coexistence of superconductivity and antiferromagnetic order in the compound Er$_{2}$O$_{2}$Bi with anti-ThCr$_{2}$Si$_{2}$-type structure through resistivity, magnetization, specific heat measurements and first-principle calculations. The superconducting transition temperature $T_{\rm c}$ of 1.23 K and antiferromagnetic transition temperature $T_{\rm N}$ of 3 K are observed in the sample with the best nominal composition. The superconducting upper critical field $H_{\rm c2}$(0) and electron-phonon coupling constant $λ$$_{e-ph}$ in Er$_{2}$O$_{2}$Bi are similar to those in the previously reported non-magnetic superconductor Y$_{2}$O$_{2}$Bi with the same structure, indicating that the superconductivity in Er$_{2}$O$_{2}$Bi may have the same origin as in Y$_{2}$O$_{2}$Bi. The first-principle calculations of Er$_{2}$O$_{2}$Bi show that the Fermi surface is mainly composed of the Bi 6$p$ orbitals both in the paramagnetic and antiferromagnetic state, implying minor effect of the 4$f$ electrons on the Fermi surface. Besides, upon increasing the oxygen incorporation in Er$_{2}$O$_{x}$Bi, $T_{\rm c}$ increases from 1 to 1.23 K and $T_{\rm N}$ decreases slightly from 3 to 2.96 K, revealing that superconductivity and antiferromagnetic order may compete with each other. The Hall effect measurements indicate that hole-type carrier density indeed increases with increasing oxygen content, which may account for the variations of $T_{\rm c}$ and $T_{\rm N}$ with different oxygen content.

cond-mat.supr-con↗

arXiv2021

Superconductivity in ThMo2Si2C with Mo2C Square Net

We report the superconductivity of a new quaternary compound ThMo$_2$Si$_2$C, synthesized with the arc-melting technique. The compound crystallizes in a tetragonal CeCr$_2$Si$_2$C-type structure with cell parameters of $a$ = 4.2296 Åand $c$ = 5.3571 Å. An interlayer Si-Si covalent bonding is suggested by the atomic distance. The electrical resistivity and magnetic susceptibility measurements indicate a Pauli-paramagnetic metal with dominant electron-electron scattering in the normal-state. Bulk superconductivity at 2.2 K is demonstrated with a dimensionless specific-heat jump of $ΔC/γ_{\rm n}T$ = 0.98. The superconducting parameters of the critical magnetic fields, coherence length, penetration depth, and superconducting energy gap are given.

cond-mat.supr-con↗

arXiv2020

Enhanced anisotropic superconductivity in the topological nodal-line semimetal InxTaS2

Coexistence of topological bands and charge density wave (CDW) in topological materials has attracted immense attentions because of their fantastic properties, such as axionic-CDW, three-dimensional quantum Hall effect, etc. In this work, a nodal-line semimetal InxTaS2 characterized by CDW and superconductivity is successfully synthesized, whose structure and topological bands (two separated Wely rings) are similar to In0.58TaSe2. A 2 x 2 commensurate CDW is observed at low temperature in InxTaS2, identified by transport properties and STM measurements. Moreover, superconductivity emerges below 0.69 K, and the anisotropy ratio of upper critical field [Gamma = H||ab c2(0)=H||c c2(0)] is significantly enhanced compared to 2H-TaS2, which shares the same essential layer unit. According to the Lawrence-Doniach model, the enhanced Gamma may be explained by the reduced effective mass in kx-ky plane, where Weyl rings locate. Therefore, this type of layered topological systems may offer a platform to investigate highly anisotropic superconductivity and to understand the extremely large upper critical field in the bulk or in the two-dimensional limit.

cond-mat.supr-con↗